mirror of https://github.com/gsi-upm/senpy
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@ -1,22 +0,0 @@
|
||||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
|
||||
|
||||
version: 2
|
||||
|
||||
# Set the OS, Python version and other tools you might need
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||||
|
||||
build:
|
||||
os: ubuntu-22.04
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||||
tools:
|
||||
python: "3.10"
|
||||
|
||||
# Build documentation in the "docs/" directory with Sphinx
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sphinx:
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configuration: docs/conf.py
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||||
|
||||
# formats:
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||||
# - pdf
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||||
# - epub
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||||
|
||||
python:
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install:
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||||
- requirements: docs/requirements.txt
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@ -1,4 +1,3 @@
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module: mynoop
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optional: true
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||||
requirements:
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- noop
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||||
@ -1,29 +1,21 @@
|
||||
---
|
||||
apiVersion: networking.k8s.io/v1
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||||
apiVersion: extensions/v1beta1
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kind: Ingress
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metadata:
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name: senpy-ingress
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labels:
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app: senpy-latest
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spec:
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rules:
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- host: senpy-latest.gsi.upm.es
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- host: latest.senpy.cluster.gsi.dit.upm.es
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http:
|
||||
paths:
|
||||
- path: /
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pathType: Prefix
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backend:
|
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service:
|
||||
name: senpy-latest
|
||||
port:
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||||
number: 5000
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serviceName: senpy-latest
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servicePort: 5000
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- host: latest.senpy.gsi.upm.es
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http:
|
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paths:
|
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- path: /
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pathType: Prefix
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backend:
|
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service:
|
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name: senpy-latest
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port:
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number: 5000
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serviceName: senpy-latest
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servicePort: 5000
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@ -1,7 +0,0 @@
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import os
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strict = os.environ.get('SENPY_STRICT', '').lower() not in ["", "false", "f"]
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data_folder = os.environ.get('SENPY_DATA', None)
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if data_folder:
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data_folder = os.path.abspath(data_folder)
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testing = os.environ.get('SENPY_TESTING', "") != ""
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@ -1,60 +0,0 @@
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# Plugin emotion-anew
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This plugin consists on an **emotion classifier** that detects six possible emotions:
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- Anger : general-dislike.
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- Fear : negative-fear.
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- Disgust : shame.
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- Joy : gratitude, affective, enthusiasm, love, joy, liking.
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- Sadness : ingrattitude, daze, humlity, compassion, despair, anxiety, sadness.
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- Neutral: not detected a particulary emotion.
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The plugin uses **ANEW lexicon** dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. To do this comparision, it is defined that each emotion has a centroid, calculated according to this article: http://www.aclweb.org/anthology/W10-0208.
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The plugin is going to look for the words in the sentence that appear in the ANEW dictionary and calculate the average VAD score for the sentence. Once this score is calculated, it is going to seek the emotion that is closest to this value.
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The response of this plugin uses [Onyx ontology](https://www.gsi.dit.upm.es/ontologies/onyx/) developed at GSI UPM, to express the information.
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## Installation
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* Download
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```
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git clone https://lab.cluster.gsi.dit.upm.es/senpy/emotion-anew.git
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```
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* Get data
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```
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cd emotion-anew
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git submodule update --init --recursive
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```
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* Run
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```
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docker run -p 5000:5000 -v $PWD:/plugins gsiupm/senpy:python2.7 -f /plugins
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```
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## Data format
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`data/Corpus/affective-isear.tsv` contains data from ISEAR Databank: http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615
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##Usage
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Params accepted:
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- Language: English (en) and Spanish (es).
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- Input: input text to analyse.
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Example request:
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```
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http://senpy.cluster.gsi.dit.upm.es/api/?algo=emotion-anew&language=en&input=I%20love%20Madrid
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```
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Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section.
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# Known issues
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- To obtain Anew dictionary you can download from here: <https://github.com/hcorona/SMC2015/blob/master/resources/ANEW2010All.txt>
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- This plugin only supports **Python2**
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![alt GSI Logo][logoGSI]
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[logoES]: https://www.gsi.dit.upm.es/ontologies/onyx/img/eurosentiment_logo.png "EuroSentiment logo"
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[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"
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@ -1,269 +0,0 @@
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# -*- coding: utf-8 -*-
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import re
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import nltk
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import csv
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import sys
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import os
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import unicodedata
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import string
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import xml.etree.ElementTree as ET
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import math
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from sklearn.svm import LinearSVC
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from sklearn.feature_extraction import DictVectorizer
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from nltk import bigrams
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from nltk import trigrams
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from nltk.corpus import stopwords
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from pattern.en import parse as parse_en
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from pattern.es import parse as parse_es
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from senpy.plugins import EmotionPlugin, SenpyPlugin
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from senpy.models import Results, EmotionSet, Entry, Emotion
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### BEGIN WORKAROUND FOR PATTERN
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# See: https://github.com/clips/pattern/issues/308
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|
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import os.path
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import pattern.text
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from pattern.helpers import decode_string
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from codecs import BOM_UTF8
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BOM_UTF8 = BOM_UTF8.decode("utf-8")
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decode_utf8 = decode_string
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MODEL = "emoml:pad-dimensions_"
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VALENCE = f"{MODEL}_valence"
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AROUSAL = f"{MODEL}_arousal"
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DOMINANCE = f"{MODEL}_dominance"
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def _read(path, encoding="utf-8", comment=";;;"):
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||||
"""Returns an iterator over the lines in the file at the given path,
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||||
strippping comments and decoding each line to Unicode.
|
||||
"""
|
||||
if path:
|
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if isinstance(path, str) and os.path.exists(path):
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||||
# From file path.
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f = open(path, "r", encoding="utf-8")
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||||
elif isinstance(path, str):
|
||||
# From string.
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||||
f = path.splitlines()
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else:
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# From file or buffer.
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f = path
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for i, line in enumerate(f):
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line = line.strip(BOM_UTF8) if i == 0 and isinstance(line, str) else line
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||||
line = line.strip()
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line = decode_utf8(line, encoding)
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if not line or (comment and line.startswith(comment)):
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continue
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yield line
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pattern.text._read = _read
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## END WORKAROUND
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class ANEW(EmotionPlugin):
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description = "This plugin consists on an emotion classifier using ANEW lexicon dictionary. It averages the VAD (valence-arousal-dominance) value of each word in the text that is also in the ANEW dictionary. To obtain a categorical value (e.g., happy) use the emotion conversion API (e.g., `emotion-model=emoml:big6`)."
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author = "@icorcuera"
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version = "0.5.2"
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name = "emotion-anew"
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||||
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extra_params = {
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||||
"language": {
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||||
"description": "language of the input",
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||||
"aliases": ["language", "l"],
|
||||
"required": True,
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||||
"options": ["es","en"],
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||||
"default": "en"
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||||
}
|
||||
}
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||||
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||||
anew_path_es = "Dictionary/Redondo(2007).csv"
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anew_path_en = "Dictionary/ANEW2010All.txt"
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onyx__usesEmotionModel = MODEL
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||||
nltk_resources = ['stopwords']
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||||
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||||
def activate(self, *args, **kwargs):
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self._stopwords = stopwords.words('english')
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dictionary={}
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dictionary['es'] = {}
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with self.open(self.anew_path_es,'r') as tabfile:
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reader = csv.reader(tabfile, delimiter='\t')
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for row in reader:
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dictionary['es'][row[2]]={}
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dictionary['es'][row[2]]['V']=row[3]
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dictionary['es'][row[2]]['A']=row[5]
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dictionary['es'][row[2]]['D']=row[7]
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dictionary['en'] = {}
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with self.open(self.anew_path_en,'r') as tabfile:
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reader = csv.reader(tabfile, delimiter='\t')
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for row in reader:
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dictionary['en'][row[0]]={}
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||||
dictionary['en'][row[0]]['V']=row[2]
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||||
dictionary['en'][row[0]]['A']=row[4]
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dictionary['en'][row[0]]['D']=row[6]
|
||||
self._dictionary = dictionary
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||||
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||||
def _my_preprocessor(self, text):
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||||
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||||
regHttp = re.compile('(http://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
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regHttps = re.compile('(https://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
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||||
regAt = re.compile('@([a-zA-Z0-9]*[*_/&%#@$]*)*[a-zA-Z0-9]*')
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||||
text = re.sub(regHttp, '', text)
|
||||
text = re.sub(regAt, '', text)
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||||
text = re.sub('RT : ', '', text)
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||||
text = re.sub(regHttps, '', text)
|
||||
text = re.sub('[0-9]', '', text)
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||||
text = self._delete_punctuation(text)
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||||
return text
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||||
|
||||
def _delete_punctuation(self, text):
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|
||||
exclude = set(string.punctuation)
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||||
s = ''.join(ch for ch in text if ch not in exclude)
|
||||
return s
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||||
|
||||
def _extract_ngrams(self, text, lang):
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||||
unigrams_lemmas = []
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||||
unigrams_words = []
|
||||
pos_tagged = []
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||||
if lang == 'es':
|
||||
sentences = list(parse_es(text, lemmata=True).split())
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||||
else:
|
||||
sentences = list(parse_en(text, lemmata=True).split())
|
||||
|
||||
for sentence in sentences:
|
||||
for token in sentence:
|
||||
if token[0].lower() not in self._stopwords:
|
||||
unigrams_words.append(token[0].lower())
|
||||
unigrams_lemmas.append(token[4])
|
||||
pos_tagged.append(token[1])
|
||||
|
||||
return unigrams_lemmas,unigrams_words,pos_tagged
|
||||
|
||||
def _find_ngrams(self, input_list, n):
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||||
return zip(*[input_list[i:] for i in range(n)])
|
||||
|
||||
def _extract_features(self, tweet,dictionary,lang):
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||||
feature_set={}
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||||
ngrams_lemmas,ngrams_words,pos_tagged = self._extract_ngrams(tweet,lang)
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||||
pos_tags={'NN':'NN', 'NNS':'NN', 'JJ':'JJ', 'JJR':'JJ', 'JJS':'JJ', 'RB':'RB', 'RBR':'RB',
|
||||
'RBS':'RB', 'VB':'VB', 'VBD':'VB', 'VGB':'VB', 'VBN':'VB', 'VBP':'VB', 'VBZ':'VB'}
|
||||
totalVAD=[0,0,0]
|
||||
matches=0
|
||||
for word in range(len(ngrams_lemmas)):
|
||||
VAD=[]
|
||||
if ngrams_lemmas[word] in dictionary:
|
||||
matches+=1
|
||||
totalVAD = [totalVAD[0]+float(dictionary[ngrams_lemmas[word]]['V']),
|
||||
totalVAD[1]+float(dictionary[ngrams_lemmas[word]]['A']),
|
||||
totalVAD[2]+float(dictionary[ngrams_lemmas[word]]['D'])]
|
||||
elif ngrams_words[word] in dictionary:
|
||||
matches+=1
|
||||
totalVAD = [totalVAD[0]+float(dictionary[ngrams_words[word]]['V']),
|
||||
totalVAD[1]+float(dictionary[ngrams_words[word]]['A']),
|
||||
totalVAD[2]+float(dictionary[ngrams_words[word]]['D'])]
|
||||
if matches==0:
|
||||
emotion='neutral'
|
||||
else:
|
||||
totalVAD=[totalVAD[0]/matches,totalVAD[1]/matches,totalVAD[2]/matches]
|
||||
feature_set['V'] = totalVAD[0]
|
||||
feature_set['A'] = totalVAD[1]
|
||||
feature_set['D'] = totalVAD[2]
|
||||
return feature_set
|
||||
|
||||
def analyse_entry(self, entry, activity):
|
||||
params = activity.params
|
||||
|
||||
text_input = entry.text
|
||||
|
||||
text = self._my_preprocessor(text_input)
|
||||
dictionary = self._dictionary[params['language']]
|
||||
|
||||
feature_set=self._extract_features(text, dictionary, params['language'])
|
||||
|
||||
emotions = EmotionSet()
|
||||
emotions.id = "Emotions0"
|
||||
|
||||
emotion1 = Emotion(id="Emotion0")
|
||||
emotion1[VALENCE] = feature_set['V']
|
||||
emotion1[AROUSAL] = feature_set['A']
|
||||
emotion1[DOMINANCE] = feature_set['D']
|
||||
|
||||
emotion1.prov(activity)
|
||||
emotions.prov(activity)
|
||||
|
||||
emotions.onyx__hasEmotion.append(emotion1)
|
||||
entry.emotions = [emotions, ]
|
||||
|
||||
yield entry
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'name': 'anger with VAD=(2.12, 6.95, 5.05)',
|
||||
'input': 'I hate you',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
AROUSAL: 6.95,
|
||||
DOMINANCE: 5.05,
|
||||
VALENCE: 2.12,
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'input': 'i am sad',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
f"{MODEL}_arousal": 4.13,
|
||||
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'name': 'joy',
|
||||
'input': 'i am happy with my marks',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
AROUSAL: 6.49,
|
||||
DOMINANCE: 6.63,
|
||||
VALENCE: 8.21,
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'name': 'negative-feat',
|
||||
'input': 'This movie is scary',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
AROUSAL: 5.8100000000000005,
|
||||
DOMINANCE: 4.33,
|
||||
VALENCE: 5.050000000000001,
|
||||
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'name': 'negative-fear',
|
||||
'input': 'this cake is disgusting' ,
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
AROUSAL: 5.09,
|
||||
DOMINANCE: 4.4,
|
||||
VALENCE: 5.109999999999999,
|
||||
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -1,12 +0,0 @@
|
||||
---
|
||||
module: emotion-anew
|
||||
optional: true
|
||||
requirements:
|
||||
- numpy
|
||||
- pandas
|
||||
- nltk
|
||||
- scipy
|
||||
- scikit-learn
|
||||
- textblob
|
||||
- pattern
|
||||
- lxml
|
||||
@ -1,179 +0,0 @@
|
||||
#!/usr/local/bin/python
|
||||
# coding: utf-8
|
||||
|
||||
from future import standard_library
|
||||
standard_library.install_aliases()
|
||||
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
import string
|
||||
import numpy as np
|
||||
from six.moves import urllib
|
||||
from nltk.corpus import stopwords
|
||||
|
||||
from senpy import EmotionBox, models
|
||||
|
||||
|
||||
def ignore(dchars):
|
||||
deletechars = "".join(dchars)
|
||||
tbl = str.maketrans("", "", deletechars)
|
||||
ignore = lambda s: s.translate(tbl)
|
||||
return ignore
|
||||
|
||||
|
||||
class DepecheMood(EmotionBox):
|
||||
'''
|
||||
Plugin that uses the DepecheMood emotion lexicon.
|
||||
|
||||
DepecheMood is an emotion lexicon automatically generated from news articles where users expressed their associated emotions. It contains two languages (English and Italian), as well as three types of word representations (token, lemma and lemma#PoS). For English, the lexicon contains 165k tokens, while the Italian version contains 116k. Unsupervised techniques can be applied to generate simple but effective baselines. To learn more, please visit https://github.com/marcoguerini/DepecheMood and http://www.depechemood.eu/
|
||||
'''
|
||||
|
||||
author = 'Oscar Araque'
|
||||
name = 'emotion-depechemood'
|
||||
version = '0.1'
|
||||
requirements = ['pandas']
|
||||
optional = True
|
||||
nltk_resources = ["stopwords"]
|
||||
|
||||
onyx__usesEmotionModel = 'wna:WNAModel'
|
||||
|
||||
EMOTIONS = ['wna:negative-fear',
|
||||
'wna:amusement',
|
||||
'wna:anger',
|
||||
'wna:annoyance',
|
||||
'wna:indifference',
|
||||
'wna:joy',
|
||||
'wna:awe',
|
||||
'wna:sadness']
|
||||
|
||||
DM_EMOTIONS = ['AFRAID', 'AMUSED', 'ANGRY', 'ANNOYED', 'DONT_CARE', 'HAPPY', 'INSPIRED', 'SAD',]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(DepecheMood, self).__init__(*args, **kwargs)
|
||||
self.LEXICON_URL = "https://github.com/marcoguerini/DepecheMood/raw/master/DepecheMood%2B%2B/DepecheMood_english_token_full.tsv"
|
||||
self._denoise = ignore(set(string.punctuation)|set('«»'))
|
||||
self._stop_words = []
|
||||
self._lex_vocab = None
|
||||
self._lex = None
|
||||
|
||||
def activate(self):
|
||||
self._lex = self.download_lex()
|
||||
self._lex_vocab = set(list(self._lex.keys()))
|
||||
self._stop_words = stopwords.words('english') + ['']
|
||||
|
||||
def clean_str(self, string):
|
||||
string = re.sub(r"[^A-Za-z0-9().,!?\'\`]", " ", string)
|
||||
string = re.sub(r"[0-9]+", " num ", string)
|
||||
string = re.sub(r"\'s", " \'s", string)
|
||||
string = re.sub(r"\'ve", " \'ve", string)
|
||||
string = re.sub(r"n\'t", " n\'t", string)
|
||||
string = re.sub(r"\'re", " \'re", string)
|
||||
string = re.sub(r"\'d", " \'d", string)
|
||||
string = re.sub(r"\'ll", " \'ll", string)
|
||||
string = re.sub(r"\.", " . ", string)
|
||||
string = re.sub(r",", " , ", string)
|
||||
string = re.sub(r"!", " ! ", string)
|
||||
string = re.sub(r"\(", " ( ", string)
|
||||
string = re.sub(r"\)", " ) ", string)
|
||||
string = re.sub(r"\?", " ? ", string)
|
||||
string = re.sub(r"\s{2,}", " ", string)
|
||||
return string.strip().lower()
|
||||
|
||||
def preprocess(self, text):
|
||||
if text is None:
|
||||
return None
|
||||
tokens = self._denoise(self.clean_str(text)).split(' ')
|
||||
tokens = [tok for tok in tokens if tok not in self._stop_words]
|
||||
return tokens
|
||||
|
||||
def estimate_emotion(self, tokens, emotion):
|
||||
s = []
|
||||
for tok in tokens:
|
||||
s.append(self._lex[tok][emotion])
|
||||
dividend = np.sum(s) if np.sum(s) > 0 else 0
|
||||
divisor = len(s) if len(s) > 0 else 1
|
||||
S = np.sum(s) / divisor
|
||||
return S
|
||||
|
||||
def estimate_all_emotions(self, tokens):
|
||||
S = []
|
||||
intersection = set(tokens) & self._lex_vocab
|
||||
for emotion in self.DM_EMOTIONS:
|
||||
s = self.estimate_emotion(intersection, emotion)
|
||||
S.append(s)
|
||||
return S
|
||||
|
||||
def download_lex(self, file_path='DepecheMood_english_token_full.tsv', freq_threshold=10):
|
||||
|
||||
import pandas as pd
|
||||
|
||||
try:
|
||||
file_path = self.find_file(file_path)
|
||||
except IOError:
|
||||
file_path = self.path(file_path)
|
||||
filename, _ = urllib.request.urlretrieve(self.LEXICON_URL, file_path)
|
||||
|
||||
lexicon = pd.read_csv(file_path, sep='\t', index_col=0)
|
||||
lexicon = lexicon[lexicon['freq'] >= freq_threshold]
|
||||
lexicon.drop('freq', axis=1, inplace=True)
|
||||
lexicon = lexicon.T.to_dict()
|
||||
return lexicon
|
||||
|
||||
def predict_one(self, features, **kwargs):
|
||||
tokens = self.preprocess(features[0])
|
||||
estimation = self.estimate_all_emotions(tokens)
|
||||
return estimation
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'entry': {
|
||||
'nif:isString': 'My cat is very happy',
|
||||
},
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [
|
||||
{
|
||||
'onyx:hasEmotion': [
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:negative-fear',
|
||||
'onyx:hasEmotionIntensity': 0.05278117640010922
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:amusement',
|
||||
'onyx:hasEmotionIntensity': 0.2114806151413433,
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:anger',
|
||||
'onyx:hasEmotionIntensity': 0.05726119426520887
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:annoyance',
|
||||
'onyx:hasEmotionIntensity': 0.12295990731053638,
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:indifference',
|
||||
'onyx:hasEmotionIntensity': 0.1860159893608025,
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:joy',
|
||||
'onyx:hasEmotionIntensity': 0.12904050973724163,
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:awe',
|
||||
'onyx:hasEmotionIntensity': 0.17973650399862967,
|
||||
},
|
||||
{
|
||||
'onyx:hasEmotionCategory': 'wna:sadness',
|
||||
'onyx:hasEmotionIntensity': 0.060724103786128455,
|
||||
},
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from senpy.utils import easy_test
|
||||
easy_test(debug=False)
|
||||
@ -1,3 +0,0 @@
|
||||
FROM gsiupm/senpy:python{{PYVERSION}}
|
||||
|
||||
MAINTAINER manuel.garcia-amado.sancho@alumnos.upm.es
|
||||
@ -1,9 +0,0 @@
|
||||
NAME:=wnaffect
|
||||
VERSIONFILE:=VERSION
|
||||
IMAGENAME:=registry.cluster.gsi.dit.upm.es/senpy/emotion-wnaffect
|
||||
PYVERSIONS:=2.7 3.5
|
||||
DEVPORT:=5000
|
||||
|
||||
include .makefiles/base.mk
|
||||
include .makefiles/k8s.mk
|
||||
include .makefiles/python.mk
|
||||
@ -1,62 +0,0 @@
|
||||
# WordNet-Affect plugin
|
||||
|
||||
This plugin uses WordNet-Affect (http://wndomains.fbk.eu/wnaffect.html) to calculate the percentage of each emotion. The plugin classifies among five diferent emotions: anger, fear, disgust, joy and sadness. It is has been used a emotion mapping enlarge the emotions:
|
||||
|
||||
- anger : general-dislike
|
||||
- fear : negative-fear
|
||||
- disgust : shame
|
||||
- joy : gratitude, affective, enthusiasm, love, joy, liking
|
||||
- sadness : ingrattitude, daze, humlity, compassion, despair, anxiety, sadness
|
||||
|
||||
## Installation
|
||||
|
||||
* Download
|
||||
```
|
||||
git clone https://lab.cluster.gsi.dit.upm.es/senpy/emotion-wnaffect.git
|
||||
```
|
||||
* Get data
|
||||
```
|
||||
cd emotion-wnaffect
|
||||
git submodule update --init --recursive
|
||||
```
|
||||
* Run
|
||||
```
|
||||
docker run -p 5000:5000 -v $PWD:/plugins gsiupm/senpy -f /plugins
|
||||
```
|
||||
|
||||
## Data format
|
||||
|
||||
`data/a-hierarchy.xml` is a xml file
|
||||
`data/a-synsets.xml` is a xml file
|
||||
|
||||
## Usage
|
||||
|
||||
The parameters accepted are:
|
||||
|
||||
- Language: English (en).
|
||||
- Input: Text to analyse.
|
||||
|
||||
Example request:
|
||||
```
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=emotion-wnaffect&language=en&input=I%20love%20Madrid
|
||||
```
|
||||
|
||||
Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section.
|
||||
|
||||
|
||||
The response of this plugin uses [Onyx ontology](https://www.gsi.dit.upm.es/ontologies/onyx/) developed at GSI UPM for semantic web.
|
||||
|
||||
This plugin uses WNAffect labels for emotion analysis.
|
||||
|
||||
The emotion-wnaffect.senpy file can be copied and modified to use different versions of wnaffect with the same python code.
|
||||
|
||||
|
||||
## Known issues
|
||||
|
||||
- This plugin run on **Python2.7** and **Python3.5**
|
||||
- Wnaffect and corpora files are not included in the repository, but can be easily added either to the docker image (using a volume) or in a new docker image.
|
||||
- You can download Wordnet 1.6 here: <http://wordnetcode.princeton.edu/1.6/wn16.unix.tar.gz> and extract the dict folder.
|
||||
- The hierarchy and synsets files can be found here: <https://github.com/larsmans/wordnet-domains-sentiwords/tree/master/wn-domains/wn-affect-1.1>
|
||||
|
||||
![alt GSI Logo][logoGSI]
|
||||
[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"
|
||||
@ -1,278 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
from __future__ import division
|
||||
import re
|
||||
import nltk
|
||||
import os
|
||||
import string
|
||||
import xml.etree.ElementTree as ET
|
||||
from nltk.corpus import stopwords
|
||||
from nltk.corpus import WordNetCorpusReader
|
||||
from nltk.stem import wordnet
|
||||
from emotion import Emotion as Emo
|
||||
from senpy.plugins import EmotionPlugin, AnalysisPlugin, ShelfMixin
|
||||
from senpy.models import Results, EmotionSet, Entry, Emotion
|
||||
|
||||
|
||||
class WNAffect(EmotionPlugin, ShelfMixin):
|
||||
'''
|
||||
Emotion classifier using WordNet-Affect to calculate the percentage
|
||||
of each emotion. This plugin classifies among 6 emotions: anger,fear,disgust,joy,sadness
|
||||
or neutral. The only available language is English (en)
|
||||
'''
|
||||
name = 'emotion-wnaffect'
|
||||
author = ["@icorcuera", "@balkian"]
|
||||
version = '0.2'
|
||||
extra_params = {
|
||||
'language': {
|
||||
"@id": 'lang_wnaffect',
|
||||
'description': 'language of the input',
|
||||
'aliases': ['language', 'l'],
|
||||
'required': True,
|
||||
'options': ['en',]
|
||||
}
|
||||
}
|
||||
synsets_path = "a-synsets.xml"
|
||||
hierarchy_path = "a-hierarchy.xml"
|
||||
wn16_path = "wordnet1.6/dict"
|
||||
onyx__usesEmotionModel = "emoml:big6"
|
||||
nltk_resources = ['stopwords', 'averaged_perceptron_tagger', 'wordnet']
|
||||
|
||||
def _load_synsets(self, synsets_path):
|
||||
"""Returns a dictionary POS tag -> synset offset -> emotion (str -> int -> str)."""
|
||||
tree = ET.parse(synsets_path)
|
||||
root = tree.getroot()
|
||||
pos_map = {"noun": "NN", "adj": "JJ", "verb": "VB", "adv": "RB"}
|
||||
|
||||
synsets = {}
|
||||
for pos in ["noun", "adj", "verb", "adv"]:
|
||||
tag = pos_map[pos]
|
||||
synsets[tag] = {}
|
||||
for elem in root.findall(
|
||||
".//{0}-syn-list//{0}-syn".format(pos, pos)):
|
||||
offset = int(elem.get("id")[2:])
|
||||
if not offset: continue
|
||||
if elem.get("categ"):
|
||||
synsets[tag][offset] = Emo.emotions[elem.get(
|
||||
"categ")] if elem.get(
|
||||
"categ") in Emo.emotions else None
|
||||
elif elem.get("noun-id"):
|
||||
synsets[tag][offset] = synsets[pos_map["noun"]][int(
|
||||
elem.get("noun-id")[2:])]
|
||||
return synsets
|
||||
|
||||
def _load_emotions(self, hierarchy_path):
|
||||
"""Loads the hierarchy of emotions from the WordNet-Affect xml."""
|
||||
|
||||
tree = ET.parse(hierarchy_path)
|
||||
root = tree.getroot()
|
||||
for elem in root.findall("categ"):
|
||||
name = elem.get("name")
|
||||
if name == "root":
|
||||
Emo.emotions["root"] = Emo("root")
|
||||
else:
|
||||
Emo.emotions[name] = Emo(name, elem.get("isa"))
|
||||
|
||||
def activate(self, *args, **kwargs):
|
||||
|
||||
self._stopwords = stopwords.words('english')
|
||||
self._wnlemma = wordnet.WordNetLemmatizer()
|
||||
self._syntactics = {'N': 'n', 'V': 'v', 'J': 'a', 'S': 's', 'R': 'r'}
|
||||
local_path = os.environ.get("SENPY_DATA")
|
||||
self._categories = {
|
||||
'anger': [
|
||||
'general-dislike',
|
||||
],
|
||||
'fear': [
|
||||
'negative-fear',
|
||||
],
|
||||
'disgust': [
|
||||
'shame',
|
||||
],
|
||||
'joy':
|
||||
['gratitude', 'affective', 'enthusiasm', 'love', 'joy', 'liking'],
|
||||
'sadness': [
|
||||
'ingrattitude', 'daze', 'humility', 'compassion', 'despair',
|
||||
'anxiety', 'sadness'
|
||||
]
|
||||
}
|
||||
|
||||
self._wnaffect_mappings = {
|
||||
'anger': 'anger',
|
||||
'fear': 'negative-fear',
|
||||
'disgust': 'disgust',
|
||||
'joy': 'joy',
|
||||
'sadness': 'sadness'
|
||||
}
|
||||
|
||||
self._load_emotions(self.find_file(self.hierarchy_path))
|
||||
|
||||
if 'total_synsets' not in self.sh:
|
||||
total_synsets = self._load_synsets(self.find_file(self.synsets_path))
|
||||
self.sh['total_synsets'] = total_synsets
|
||||
|
||||
self._total_synsets = self.sh['total_synsets']
|
||||
|
||||
self._wn16_path = self.wn16_path
|
||||
self._wn16 = WordNetCorpusReader(self.find_file(self._wn16_path), nltk.data.find(self.find_file(self._wn16_path)))
|
||||
|
||||
|
||||
def deactivate(self, *args, **kwargs):
|
||||
self.save(ignore_errors=True)
|
||||
|
||||
def _my_preprocessor(self, text):
|
||||
|
||||
regHttp = re.compile(
|
||||
'(http://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
|
||||
regHttps = re.compile(
|
||||
'(https://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
|
||||
regAt = re.compile('@([a-zA-Z0-9]*[*_/&%#@$]*)*[a-zA-Z0-9]*')
|
||||
text = re.sub(regHttp, '', text)
|
||||
text = re.sub(regAt, '', text)
|
||||
text = re.sub('RT : ', '', text)
|
||||
text = re.sub(regHttps, '', text)
|
||||
text = re.sub('[0-9]', '', text)
|
||||
text = self._delete_punctuation(text)
|
||||
return text
|
||||
|
||||
def _delete_punctuation(self, text):
|
||||
|
||||
exclude = set(string.punctuation)
|
||||
s = ''.join(ch for ch in text if ch not in exclude)
|
||||
return s
|
||||
|
||||
def _extract_ngrams(self, text):
|
||||
|
||||
unigrams_lemmas = []
|
||||
pos_tagged = []
|
||||
unigrams_words = []
|
||||
tokens = text.split()
|
||||
for token in nltk.pos_tag(tokens):
|
||||
unigrams_words.append(token[0])
|
||||
pos_tagged.append(token[1])
|
||||
if token[1][0] in self._syntactics.keys():
|
||||
unigrams_lemmas.append(
|
||||
self._wnlemma.lemmatize(token[0], self._syntactics[token[1]
|
||||
[0]]))
|
||||
else:
|
||||
unigrams_lemmas.append(token[0])
|
||||
|
||||
return unigrams_words, unigrams_lemmas, pos_tagged
|
||||
|
||||
def _find_ngrams(self, input_list, n):
|
||||
return zip(*[input_list[i:] for i in range(n)])
|
||||
|
||||
def _clean_pos(self, pos_tagged):
|
||||
|
||||
pos_tags = {
|
||||
'NN': 'NN',
|
||||
'NNP': 'NN',
|
||||
'NNP-LOC': 'NN',
|
||||
'NNS': 'NN',
|
||||
'JJ': 'JJ',
|
||||
'JJR': 'JJ',
|
||||
'JJS': 'JJ',
|
||||
'RB': 'RB',
|
||||
'RBR': 'RB',
|
||||
'RBS': 'RB',
|
||||
'VB': 'VB',
|
||||
'VBD': 'VB',
|
||||
'VGB': 'VB',
|
||||
'VBN': 'VB',
|
||||
'VBP': 'VB',
|
||||
'VBZ': 'VB'
|
||||
}
|
||||
|
||||
for i in range(len(pos_tagged)):
|
||||
if pos_tagged[i] in pos_tags:
|
||||
pos_tagged[i] = pos_tags[pos_tagged[i]]
|
||||
return pos_tagged
|
||||
|
||||
def _extract_features(self, text):
|
||||
|
||||
feature_set = {k: 0 for k in self._categories}
|
||||
ngrams_words, ngrams_lemmas, pos_tagged = self._extract_ngrams(text)
|
||||
matches = 0
|
||||
pos_tagged = self._clean_pos(pos_tagged)
|
||||
|
||||
tag_wn = {
|
||||
'NN': self._wn16.NOUN,
|
||||
'JJ': self._wn16.ADJ,
|
||||
'VB': self._wn16.VERB,
|
||||
'RB': self._wn16.ADV
|
||||
}
|
||||
for i in range(len(pos_tagged)):
|
||||
if pos_tagged[i] in tag_wn:
|
||||
synsets = self._wn16.synsets(ngrams_words[i],
|
||||
tag_wn[pos_tagged[i]])
|
||||
if synsets:
|
||||
offset = synsets[0].offset()
|
||||
if offset in self._total_synsets[pos_tagged[i]]:
|
||||
if self._total_synsets[pos_tagged[i]][offset] is None:
|
||||
continue
|
||||
else:
|
||||
emotion = self._total_synsets[pos_tagged[i]][
|
||||
offset].get_level(5).name
|
||||
matches += 1
|
||||
for i in self._categories:
|
||||
if emotion in self._categories[i]:
|
||||
feature_set[i] += 1
|
||||
if matches == 0:
|
||||
matches = 1
|
||||
|
||||
for i in feature_set:
|
||||
feature_set[i] = (feature_set[i] / matches)
|
||||
|
||||
return feature_set
|
||||
|
||||
def analyse_entry(self, entry, activity):
|
||||
params = activity.params
|
||||
|
||||
text_input = entry['nif:isString']
|
||||
|
||||
text = self._my_preprocessor(text_input)
|
||||
|
||||
feature_text = self._extract_features(text)
|
||||
|
||||
emotionSet = EmotionSet(id="Emotions0")
|
||||
emotions = emotionSet.onyx__hasEmotion
|
||||
|
||||
for i in feature_text:
|
||||
emotions.append(
|
||||
Emotion(
|
||||
onyx__hasEmotionCategory=self._wnaffect_mappings[i],
|
||||
onyx__hasEmotionIntensity=feature_text[i]))
|
||||
|
||||
entry.emotions = [emotionSet]
|
||||
|
||||
yield entry
|
||||
|
||||
|
||||
def test(self, *args, **kwargs):
|
||||
results = list()
|
||||
params = {'algo': 'emotion-wnaffect',
|
||||
'intype': 'direct',
|
||||
'expanded-jsonld': 0,
|
||||
'informat': 'text',
|
||||
'prefix': '',
|
||||
'plugin_type': 'analysisPlugin',
|
||||
'urischeme': 'RFC5147String',
|
||||
'outformat': 'json-ld',
|
||||
'i': 'Hello World',
|
||||
'input': 'Hello World',
|
||||
'conversion': 'full',
|
||||
'language': 'en',
|
||||
'algorithm': 'emotion-wnaffect'}
|
||||
|
||||
self.activate()
|
||||
texts = {'I hate you': 'anger',
|
||||
'i am sad': 'sadness',
|
||||
'i am happy with my marks': 'joy',
|
||||
'This movie is scary': 'negative-fear'}
|
||||
|
||||
for text in texts:
|
||||
response = next(self.analyse_entry(Entry(nif__isString=text),
|
||||
self.activity(params)))
|
||||
expected = texts[text]
|
||||
emotionSet = response.emotions[0]
|
||||
max_emotion = max(emotionSet['onyx:hasEmotion'], key=lambda x: x['onyx:hasEmotionIntensity'])
|
||||
assert max_emotion['onyx:hasEmotionCategory'] == expected
|
||||
@ -1,7 +0,0 @@
|
||||
---
|
||||
module: emotion-wnaffect
|
||||
optional: true
|
||||
requirements:
|
||||
- nltk>=3.0.5
|
||||
- lxml>=3.4.2
|
||||
async: false
|
||||
@ -1,95 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Clement Michard (c) 2015
|
||||
"""
|
||||
|
||||
class Emotion:
|
||||
"""Defines an emotion."""
|
||||
|
||||
emotions = {} # name to emotion (str -> Emotion)
|
||||
|
||||
def __init__(self, name, parent_name=None):
|
||||
"""Initializes an Emotion object.
|
||||
name -- name of the emotion (str)
|
||||
parent_name -- name of the parent emotion (str)
|
||||
"""
|
||||
|
||||
self.name = name
|
||||
self.parent = None
|
||||
self.level = 0
|
||||
self.children = []
|
||||
|
||||
if parent_name:
|
||||
self.parent = Emotion.emotions[parent_name] if parent_name else None
|
||||
self.parent.children.append(self)
|
||||
self.level = self.parent.level + 1
|
||||
|
||||
|
||||
def get_level(self, level):
|
||||
"""Returns the parent of self at the given level.
|
||||
level -- level in the hierarchy (int)
|
||||
"""
|
||||
|
||||
em = self
|
||||
while em.level > level and em.level >= 0:
|
||||
em = em.parent
|
||||
return em
|
||||
|
||||
|
||||
def __str__(self):
|
||||
"""Returns the emotion string formatted."""
|
||||
|
||||
return self.name
|
||||
|
||||
|
||||
def nb_children(self):
|
||||
"""Returns the number of children of the emotion."""
|
||||
|
||||
return sum(child.nb_children() for child in self.children) + 1
|
||||
|
||||
|
||||
@staticmethod
|
||||
def printTree(emotion=None, indent="", last='updown'):
|
||||
"""Prints the hierarchy of emotions.
|
||||
emotion -- root emotion (Emotion)
|
||||
"""
|
||||
|
||||
if not emotion:
|
||||
emotion = Emotion.emotions["root"]
|
||||
|
||||
size_branch = {child: child.nb_children() for child in emotion.children}
|
||||
leaves = sorted(emotion.children, key=lambda emotion: emotion.nb_children())
|
||||
up, down = [], []
|
||||
if leaves:
|
||||
while sum(size_branch[e] for e in down) < sum(size_branch[e] for e in leaves):
|
||||
down.append(leaves.pop())
|
||||
up = leaves
|
||||
|
||||
for leaf in up:
|
||||
next_last = 'up' if up.index(leaf) is 0 else ''
|
||||
next_indent = '{0}{1}{2}'.format(indent, ' ' if 'up' in last else '│', " " * len(emotion.name))
|
||||
Emotion.printTree(leaf, indent=next_indent, last=next_last)
|
||||
if last == 'up':
|
||||
start_shape = '┌'
|
||||
elif last == 'down':
|
||||
start_shape = '└'
|
||||
elif last == 'updown':
|
||||
start_shape = ' '
|
||||
else:
|
||||
start_shape = '├'
|
||||
if up:
|
||||
end_shape = '┤'
|
||||
elif down:
|
||||
end_shape = '┐'
|
||||
else:
|
||||
end_shape = ''
|
||||
print ('{0}{1}{2}{3}'.format(indent, start_shape, emotion.name, end_shape))
|
||||
for leaf in down:
|
||||
next_last = 'down' if down.index(leaf) is len(down) - 1 else ''
|
||||
next_indent = '{0}{1}{2}'.format(indent, ' ' if 'down' in last else '│', " " * len(emotion.name))
|
||||
Emotion.printTree(leaf, indent=next_indent, last=next_last)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,94 +0,0 @@
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
# In[1]:
|
||||
|
||||
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Clement Michard (c) 2015
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import nltk
|
||||
from emotion import Emotion
|
||||
from nltk.corpus import WordNetCorpusReader
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
class WNAffect:
|
||||
"""WordNet-Affect resource."""
|
||||
|
||||
nltk_resources = ['averaged_perceptron_tagger']
|
||||
|
||||
def __init__(self, wordnet16_dir, wn_domains_dir):
|
||||
"""Initializes the WordNet-Affect object."""
|
||||
|
||||
cwd = os.getcwd()
|
||||
nltk.data.path.append(cwd)
|
||||
wn16_path = "{0}/dict".format(wordnet16_dir)
|
||||
self.wn16 = WordNetCorpusReader(os.path.abspath("{0}/{1}".format(cwd, wn16_path)), nltk.data.find(wn16_path))
|
||||
self.flat_pos = {'NN':'NN', 'NNS':'NN', 'JJ':'JJ', 'JJR':'JJ', 'JJS':'JJ', 'RB':'RB', 'RBR':'RB', 'RBS':'RB', 'VB':'VB', 'VBD':'VB', 'VGB':'VB', 'VBN':'VB', 'VBP':'VB', 'VBZ':'VB'}
|
||||
self.wn_pos = {'NN':self.wn16.NOUN, 'JJ':self.wn16.ADJ, 'VB':self.wn16.VERB, 'RB':self.wn16.ADV}
|
||||
self._load_emotions(wn_domains_dir)
|
||||
self.synsets = self._load_synsets(wn_domains_dir)
|
||||
|
||||
|
||||
|
||||
def _load_synsets(self, wn_domains_dir):
|
||||
"""Returns a dictionary POS tag -> synset offset -> emotion (str -> int -> str)."""
|
||||
|
||||
tree = ET.parse("{0}/a-synsets.xml".format(wn_domains_dir))
|
||||
root = tree.getroot()
|
||||
pos_map = { "noun": "NN", "adj": "JJ", "verb": "VB", "adv": "RB" }
|
||||
|
||||
synsets = {}
|
||||
for pos in ["noun", "adj", "verb", "adv"]:
|
||||
tag = pos_map[pos]
|
||||
synsets[tag] = {}
|
||||
for elem in root.findall(".//{0}-syn-list//{0}-syn".format(pos, pos)):
|
||||
offset = int(elem.get("id")[2:])
|
||||
if not offset: continue
|
||||
if elem.get("categ"):
|
||||
synsets[tag][offset] = Emotion.emotions[elem.get("categ")] if elem.get("categ") in Emotion.emotions else None
|
||||
elif elem.get("noun-id"):
|
||||
synsets[tag][offset] = synsets[pos_map["noun"]][int(elem.get("noun-id")[2:])]
|
||||
|
||||
return synsets
|
||||
|
||||
def _load_emotions(self, wn_domains_dir):
|
||||
"""Loads the hierarchy of emotions from the WordNet-Affect xml."""
|
||||
|
||||
tree = ET.parse("{0}/a-hierarchy.xml".format(wn_domains_dir))
|
||||
root = tree.getroot()
|
||||
for elem in root.findall("categ"):
|
||||
name = elem.get("name")
|
||||
if name == "root":
|
||||
Emotion.emotions["root"] = Emotion("root")
|
||||
else:
|
||||
Emotion.emotions[name] = Emotion(name, elem.get("isa"))
|
||||
|
||||
def get_emotion(self, word, pos):
|
||||
"""Returns the emotion of the word.
|
||||
word -- the word (str)
|
||||
pos -- part-of-speech (str)
|
||||
"""
|
||||
|
||||
if pos in self.flat_pos:
|
||||
pos = self.flat_pos[pos]
|
||||
synsets = self.wn16.synsets(word, self.wn_pos[pos])
|
||||
if synsets:
|
||||
offset = synsets[0].offset()
|
||||
if offset in self.synsets[pos]:
|
||||
return self.synsets[pos][offset]
|
||||
return None
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
wordnet16, wndomains32, word, pos = sys.argv[1:5]
|
||||
wna = WNAffect(wordnet16, wndomains32)
|
||||
print wna.get_emotion(word, pos)
|
||||
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
# Sentiment basic plugin
|
||||
|
||||
This plugin is based on the classifier developed for the TASS 2015 competition. It has been developed for Spanish and English. This is a demo plugin that uses only some features from the TASS 2015 classifier. To use the entirely functional classifier you can use the service in: http://senpy.cluster.gsi.dit.upm.es
|
||||
|
||||
There is more information avaliable in:
|
||||
|
||||
- Aspect based Sentiment Analysis of Spanish Tweets, Oscar Araque and Ignacio Corcuera-Platas and Constantino Román-Gómez and Carlos A. Iglesias and J. Fernando Sánchez-Rada. http://gsi.dit.upm.es/es/investigacion/publicaciones?view=publication&task=show&id=376
|
||||
|
||||
## Usage
|
||||
Params accepted:
|
||||
|
||||
- Language: Spanish (es).
|
||||
- Input: text to analyse.
|
||||
|
||||
|
||||
Example request:
|
||||
```
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=sentiment-basic&language=es&input=I%20love%20Madrid
|
||||
```
|
||||
|
||||
Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section.
|
||||
|
||||
This plugin only supports **python2**
|
||||
|
||||
|
||||
![alt GSI Logo][logoGSI]
|
||||
|
||||
[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"
|
||||
@ -1,177 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
import os
|
||||
import sys
|
||||
import string
|
||||
import nltk
|
||||
import pickle
|
||||
|
||||
from sentiwn import SentiWordNet
|
||||
from nltk.corpus import wordnet as wn
|
||||
from textblob import TextBlob
|
||||
from scipy.interpolate import interp1d
|
||||
from os import path
|
||||
|
||||
from senpy.plugins import SentimentBox, SenpyPlugin
|
||||
from senpy.models import Results, Entry, Sentiment, Error
|
||||
|
||||
if sys.version_info[0] >= 3:
|
||||
unicode = str
|
||||
|
||||
|
||||
class SentimentBasic(SentimentBox):
|
||||
'''
|
||||
Sentiment classifier using rule-based classification for Spanish. Based on english to spanish translation and SentiWordNet sentiment knowledge. This is a demo plugin that uses only some features from the TASS 2015 classifier. To use the entirely functional classifier you can use the service in: http://senpy.cluster.gsi.dit.upm.es.
|
||||
'''
|
||||
name = "sentiment-basic"
|
||||
author = "github.com/nachtkatze"
|
||||
version = "0.1.1"
|
||||
extra_params = {
|
||||
"language": {
|
||||
"description": "language of the text",
|
||||
"aliases": ["language", "l"],
|
||||
"required": True,
|
||||
"options": ["en","es", "it", "fr"],
|
||||
"default": "en"
|
||||
}
|
||||
}
|
||||
sentiword_path = "SentiWordNet_3.0.txt"
|
||||
pos_path = "unigram_spanish.pickle"
|
||||
maxPolarityValue = 1
|
||||
minPolarityValue = -1
|
||||
nltk_resources = ['punkt','wordnet', 'omw', 'omw-1.4']
|
||||
|
||||
with_polarity = False
|
||||
|
||||
def _load_swn(self):
|
||||
self.swn_path = self.find_file(self.sentiword_path)
|
||||
swn = SentiWordNet(self.swn_path)
|
||||
return swn
|
||||
|
||||
def _load_pos_tagger(self):
|
||||
self.pos_path = self.find_file(self.pos_path)
|
||||
with open(self.pos_path, 'rb') as f:
|
||||
tagger = pickle.load(f)
|
||||
return tagger
|
||||
|
||||
def activate(self, *args, **kwargs):
|
||||
self._swn = self._load_swn()
|
||||
self._pos_tagger = self._load_pos_tagger()
|
||||
|
||||
def _remove_punctuation(self, tokens):
|
||||
return [t for t in tokens if t not in string.punctuation]
|
||||
|
||||
def _tokenize(self, text):
|
||||
sentence_ = {}
|
||||
words = nltk.word_tokenize(text)
|
||||
sentence_['sentence'] = text
|
||||
tokens_ = [w.lower() for w in words]
|
||||
sentence_['tokens'] = self._remove_punctuation(tokens_)
|
||||
return sentence_
|
||||
|
||||
def _pos(self, tokens):
|
||||
tokens['tokens'] = self._pos_tagger.tag(tokens['tokens'])
|
||||
return tokens
|
||||
|
||||
def _compare_synsets(self, synsets, tokens):
|
||||
for synset in synsets:
|
||||
for word, lemmas in tokens['lemmas'].items():
|
||||
for lemma in lemmas:
|
||||
synset_ = lemma.synset()
|
||||
if synset == synset_:
|
||||
return synset
|
||||
return None
|
||||
|
||||
def predict_one(self, features, activity):
|
||||
language = activity.param("language")
|
||||
text = features[0]
|
||||
tokens = self._tokenize(text)
|
||||
tokens = self._pos(tokens)
|
||||
sufixes = {'es':'spa','en':'eng','it':'ita','fr':'fra'}
|
||||
tokens['lemmas'] = {}
|
||||
for w in tokens['tokens']:
|
||||
lemmas = wn.lemmas(w[0], lang=sufixes[language])
|
||||
if len(lemmas) == 0:
|
||||
continue
|
||||
tokens['lemmas'][w[0]] = lemmas
|
||||
if language == "en":
|
||||
trans = TextBlob(unicode(text))
|
||||
else:
|
||||
try:
|
||||
trans = TextBlob(unicode(text)).translate(from_lang=language,to='en')
|
||||
except Exception as ex:
|
||||
raise Error('Could not translate the text from "{}" to "{}": {}'.format(language,
|
||||
'en',
|
||||
str(ex)))
|
||||
useful_synsets = {}
|
||||
for w_i, t_w in enumerate(trans.sentences[0].words):
|
||||
synsets = wn.synsets(trans.sentences[0].words[w_i])
|
||||
if len(synsets) == 0:
|
||||
continue
|
||||
eq_synset = self._compare_synsets(synsets, tokens)
|
||||
useful_synsets[t_w] = eq_synset
|
||||
scores = {}
|
||||
scores = {}
|
||||
if useful_synsets != None:
|
||||
for word in useful_synsets:
|
||||
if useful_synsets[word] is None:
|
||||
continue
|
||||
temp_scores = self._swn.get_score(useful_synsets[word].name().split('.')[0].replace(' ',' '))
|
||||
for score in temp_scores:
|
||||
if score['synset'] == useful_synsets[word]:
|
||||
t_score = score['pos'] - score['neg']
|
||||
f_score = 'neu'
|
||||
if t_score > 0:
|
||||
f_score = 'pos'
|
||||
elif t_score < 0:
|
||||
f_score = 'neg'
|
||||
score['score'] = f_score
|
||||
scores[word] = score
|
||||
break
|
||||
g_score = 0.5
|
||||
|
||||
for i in scores:
|
||||
n_pos = 0.0
|
||||
n_neg = 0.0
|
||||
for w in scores:
|
||||
if scores[w]['score'] == 'pos':
|
||||
n_pos += 1.0
|
||||
elif scores[w]['score'] == 'neg':
|
||||
n_neg += 1.0
|
||||
inter = interp1d([-1.0, 1.0], [0.0, 1.0])
|
||||
|
||||
try:
|
||||
g_score = (n_pos - n_neg) / (n_pos + n_neg)
|
||||
g_score = float(inter(g_score))
|
||||
except:
|
||||
if n_pos == 0 and n_neg == 0:
|
||||
g_score = 0.5
|
||||
|
||||
if g_score > 0.5: # Positive
|
||||
return [1, 0, 0]
|
||||
elif g_score < 0.5: # Negative
|
||||
return [0, 0, 1]
|
||||
else:
|
||||
return [0, 1, 0]
|
||||
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'input': 'Odio ir al cine',
|
||||
'params': {'language': 'es'},
|
||||
'polarity': 'marl:Negative'
|
||||
|
||||
},
|
||||
{
|
||||
'input': 'El cielo está nublado',
|
||||
'params': {'language': 'es'},
|
||||
'polarity': 'marl:Neutral'
|
||||
|
||||
},
|
||||
{
|
||||
'input': 'Esta tarta está muy buena',
|
||||
'params': {'language': 'es'},
|
||||
'polarity': 'marl:Negative' # SURPRISINGLY!
|
||||
|
||||
}
|
||||
]
|
||||
@ -1,8 +0,0 @@
|
||||
---
|
||||
module: sentiment-basic
|
||||
optional: true
|
||||
requirements:
|
||||
- nltk>=3.0.5
|
||||
- scipy>=0.14.0
|
||||
- textblob
|
||||
|
||||
@ -1,70 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
Author : Jaganadh Gopinadhan <jaganadhg@gmail.com>
|
||||
Copywright (C) : Jaganadh Gopinadhan
|
||||
|
||||
Apache License, Version 2.0
|
||||
(the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
"""
|
||||
|
||||
import sys,os
|
||||
import re
|
||||
|
||||
from nltk.corpus import wordnet
|
||||
|
||||
class SentiWordNet(object):
|
||||
"""
|
||||
Interface to SentiWordNet
|
||||
"""
|
||||
def __init__(self,swn_file):
|
||||
"""
|
||||
"""
|
||||
self.swn_file = swn_file
|
||||
self.pos_synset = self.__parse_swn_file()
|
||||
|
||||
def __parse_swn_file(self):
|
||||
"""
|
||||
Parse the SentiWordNet file and populate the POS and SynsetID hash
|
||||
"""
|
||||
pos_synset_hash = {}
|
||||
swn_data = open(self.swn_file,'r').readlines()
|
||||
head_less_swn_data = filter((lambda line: not re.search(r"^\s*#",\
|
||||
line)), swn_data)
|
||||
|
||||
for data in head_less_swn_data:
|
||||
fields = data.strip().split("\t")
|
||||
try:
|
||||
pos,syn_set_id,pos_score,neg_score,syn_set_score,\
|
||||
gloss = fields
|
||||
except:
|
||||
print("Found data without all details")
|
||||
pass
|
||||
|
||||
if pos and syn_set_score:
|
||||
pos_synset_hash[(pos,int(syn_set_id))] = (float(pos_score),\
|
||||
float(neg_score))
|
||||
|
||||
return pos_synset_hash
|
||||
|
||||
def get_score(self,word,pos=None):
|
||||
"""
|
||||
Get score for a given word/word pos combination
|
||||
"""
|
||||
senti_scores = []
|
||||
synsets = wordnet.synsets(word,pos)
|
||||
for synset in synsets:
|
||||
if (synset.pos(), synset.offset()) in self.pos_synset:
|
||||
pos_val, neg_val = self.pos_synset[(synset.pos(), synset.offset())]
|
||||
senti_scores.append({"pos":pos_val,"neg":neg_val,\
|
||||
"obj": 1.0 - (pos_val - neg_val),'synset':synset})
|
||||
|
||||
return senti_scores
|
||||
@ -1,280 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
MeaningCloud plugin uses API from Meaning Cloud to perform sentiment analysis.
|
||||
|
||||
For more information about Meaning Cloud and its services, please visit: https://www.meaningcloud.com/developer/apis
|
||||
|
||||
## Usage
|
||||
|
||||
To use this plugin, you need to obtain an API key from meaningCloud signing up here: https://www.meaningcloud.com/developer/login
|
||||
|
||||
When you had obtained the meaningCloud API Key, you have to provide it to the plugin, using the param **apiKey**.
|
||||
|
||||
To use this plugin, you should use a GET Requests with the following possible params:
|
||||
Params:
|
||||
- Language: English (en) and Spanish (es). (default: en)
|
||||
- API Key: the API key from Meaning Cloud. Aliases: ["apiKey","meaningCloud-key"]. (required)
|
||||
- Input: text to analyse.(required)
|
||||
- Model: model provided to Meaning Cloud API (for general domain). (default: general)
|
||||
|
||||
## Example of Usage
|
||||
|
||||
Example request:
|
||||
|
||||
```
|
||||
http://senpy.gsi.upm.es/api/?algo=meaningCloud&language=en&apiKey=<put here your API key>&input=I%20love%20Madrid
|
||||
```
|
||||
'''
|
||||
|
||||
import time
|
||||
import requests
|
||||
import json
|
||||
import string
|
||||
import os
|
||||
from os import path
|
||||
import time
|
||||
from senpy.plugins import SentimentPlugin
|
||||
from senpy.models import Results, Entry, Entity, Topic, Sentiment, Error
|
||||
from senpy.utils import check_template
|
||||
|
||||
|
||||
class MeaningCloudPlugin(SentimentPlugin):
|
||||
'''
|
||||
Sentiment analysis with meaningCloud service.
|
||||
To use this plugin, you need to obtain an API key from meaningCloud signing up here:
|
||||
https://www.meaningcloud.com/developer/login
|
||||
|
||||
When you had obtained the meaningCloud API Key, you have to provide it to the plugin, using param apiKey.
|
||||
Example request:
|
||||
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=meaningCloud&language=en&apiKey=YOUR_API_KEY&input=I%20love%20Madrid.
|
||||
'''
|
||||
name = 'sentiment-meaningcloud'
|
||||
author = 'GSI UPM'
|
||||
version = "1.1"
|
||||
maxPolarityValue = 1
|
||||
minPolarityValue = -1
|
||||
|
||||
extra_params = {
|
||||
"language": {
|
||||
"description": "language of the input",
|
||||
"aliases": ["language", "l"],
|
||||
"required": True,
|
||||
"options": ["en","es","ca","it","pt","fr","auto"],
|
||||
"default": "auto"
|
||||
},
|
||||
"apikey":{
|
||||
"description": "API key for the meaningcloud service. See https://www.meaningcloud.com/developer/login",
|
||||
"aliases": ["apiKey", "meaningcloud-key", "meaningcloud-apikey"],
|
||||
"required": True
|
||||
}
|
||||
}
|
||||
|
||||
def _polarity(self, value):
|
||||
|
||||
if 'NONE' in value:
|
||||
polarity = 'marl:Neutral'
|
||||
polarityValue = 0
|
||||
elif 'N' in value:
|
||||
polarity = 'marl:Negative'
|
||||
polarityValue = -1
|
||||
elif 'P' in value:
|
||||
polarity = 'marl:Positive'
|
||||
polarityValue = 1
|
||||
return polarity, polarityValue
|
||||
|
||||
def analyse_entry(self, entry, activity):
|
||||
params = activity.params
|
||||
|
||||
txt = entry['nif:isString']
|
||||
api = 'http://api.meaningcloud.com/'
|
||||
lang = params.get("language")
|
||||
model = "general"
|
||||
key = params["apikey"]
|
||||
parameters = {
|
||||
'key': key,
|
||||
'model': model,
|
||||
'lang': lang,
|
||||
'of': 'json',
|
||||
'txt': txt,
|
||||
'tt': 'a'
|
||||
}
|
||||
try:
|
||||
r = requests.post(
|
||||
api + "sentiment-2.1", params=parameters, timeout=3)
|
||||
parameters['lang'] = r.json()['model'].split('_')[1]
|
||||
lang = parameters['lang']
|
||||
r2 = requests.post(
|
||||
api + "topics-2.0", params=parameters, timeout=3)
|
||||
except requests.exceptions.Timeout:
|
||||
raise Error("Meaning Cloud API does not response")
|
||||
|
||||
api_response = r.json()
|
||||
api_response_topics = r2.json()
|
||||
if not api_response.get('score_tag'):
|
||||
raise Error(r.json())
|
||||
entry['language_detected'] = lang
|
||||
self.log.debug(api_response)
|
||||
agg_polarity, agg_polarityValue = self._polarity(
|
||||
api_response.get('score_tag', None))
|
||||
agg_opinion = Sentiment(
|
||||
id="Opinion0",
|
||||
marl__hasPolarity=agg_polarity,
|
||||
marl__polarityValue=agg_polarityValue,
|
||||
marl__opinionCount=len(api_response['sentence_list']))
|
||||
agg_opinion.prov(self)
|
||||
entry.sentiments.append(agg_opinion)
|
||||
self.log.debug(api_response['sentence_list'])
|
||||
count = 1
|
||||
|
||||
for sentence in api_response['sentence_list']:
|
||||
for nopinion in sentence['segment_list']:
|
||||
self.log.debug(nopinion)
|
||||
polarity, polarityValue = self._polarity(
|
||||
nopinion.get('score_tag', None))
|
||||
opinion = Sentiment(
|
||||
id="Opinion{}".format(count),
|
||||
marl__hasPolarity=polarity,
|
||||
marl__polarityValue=polarityValue,
|
||||
marl__aggregatesOpinion=agg_opinion.get('id'),
|
||||
nif__anchorOf=nopinion.get('text', None),
|
||||
nif__beginIndex=int(nopinion.get('inip', None)),
|
||||
nif__endIndex=int(nopinion.get('endp', None)))
|
||||
count += 1
|
||||
opinion.prov(self)
|
||||
entry.sentiments.append(opinion)
|
||||
|
||||
mapper = {'es': 'es.', 'en': '', 'ca': 'es.', 'it':'it.', 'fr':'fr.', 'pt':'pt.'}
|
||||
|
||||
for sent_entity in api_response_topics['entity_list']:
|
||||
resource = "_".join(sent_entity.get('form', None).split())
|
||||
entity = Entity(
|
||||
id="Entity{}".format(sent_entity.get('id')),
|
||||
itsrdf__taIdentRef="http://{}dbpedia.org/resource/{}".format(
|
||||
mapper[lang], resource),
|
||||
nif__anchorOf=sent_entity.get('form', None),
|
||||
nif__beginIndex=int(sent_entity['variant_list'][0].get('inip', None)),
|
||||
nif__endIndex=int(sent_entity['variant_list'][0].get('endp', None)))
|
||||
sementity = sent_entity['sementity'].get('type', None).split(">")[-1]
|
||||
entity['@type'] = "ODENTITY_{}".format(sementity)
|
||||
entity.prov(self)
|
||||
if 'senpy:hasEntity' not in entry:
|
||||
entry['senpy:hasEntity'] = []
|
||||
entry['senpy:hasEntity'].append(entity)
|
||||
|
||||
for topic in api_response_topics['concept_list']:
|
||||
if 'semtheme_list' in topic:
|
||||
for theme in topic['semtheme_list']:
|
||||
concept = Topic()
|
||||
concept.id = "Topic{}".format(topic.get('id'))
|
||||
concept['@type'] = "ODTHEME_{}".format(theme['type'].split(">")[-1])
|
||||
concept['fam:topic-reference'] = "http://dbpedia.org/resource/{}".format(theme['type'].split('>')[-1])
|
||||
entry.prov(self)
|
||||
if 'senpy:hasTopic' not in entry:
|
||||
entry['senpy:hasTopic'] = []
|
||||
entry['senpy:hasTopic'].append(concept)
|
||||
yield entry
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'params': {
|
||||
'algo': 'sentiment-meaningCloud',
|
||||
'intype': 'direct',
|
||||
'expanded-jsonld': 0,
|
||||
'informat': 'text',
|
||||
'prefix': '',
|
||||
'plugin_type': 'analysisPlugin',
|
||||
'urischeme': 'RFC5147String',
|
||||
'outformat': 'json-ld',
|
||||
'conversion': 'full',
|
||||
'language': 'en',
|
||||
'apikey': '00000',
|
||||
'algorithm': 'sentiment-meaningCloud'
|
||||
},
|
||||
'input': 'Hello World Obama',
|
||||
'expected': {
|
||||
'marl:hasOpinion': [
|
||||
{'marl:hasPolarity': 'marl:Neutral'}],
|
||||
'senpy:hasEntity': [
|
||||
{'itsrdf:taIdentRef': 'http://dbpedia.org/resource/Obama'}],
|
||||
'senpy:hasTopic': [
|
||||
{'fam:topic-reference': 'http://dbpedia.org/resource/Astronomy'}]
|
||||
},
|
||||
'responses': [
|
||||
{
|
||||
'url': 'http://api.meaningcloud.com/sentiment-2.1',
|
||||
'method': 'POST',
|
||||
'json': {
|
||||
'model': 'general_en',
|
||||
'sentence_list': [{
|
||||
'text':
|
||||
'Hello World',
|
||||
'endp':
|
||||
'10',
|
||||
'inip':
|
||||
'0',
|
||||
'segment_list': [{
|
||||
'text':
|
||||
'Hello World',
|
||||
'segment_type':
|
||||
'secondary',
|
||||
'confidence':
|
||||
'100',
|
||||
'inip':
|
||||
'0',
|
||||
'agreement':
|
||||
'AGREEMENT',
|
||||
'endp':
|
||||
'10',
|
||||
'polarity_term_list': [],
|
||||
'score_tag':
|
||||
'NONE'
|
||||
}],
|
||||
'score_tag':
|
||||
'NONE',
|
||||
}],
|
||||
'score_tag':
|
||||
'NONE'
|
||||
}
|
||||
}, {
|
||||
'url': 'http://api.meaningcloud.com/topics-2.0',
|
||||
'method': 'POST',
|
||||
'json': {
|
||||
'entity_list': [{
|
||||
'form':
|
||||
'Obama',
|
||||
'id':
|
||||
'__1265958475430276310',
|
||||
'variant_list': [{
|
||||
'endp': '16',
|
||||
'form': 'Obama',
|
||||
'inip': '12'
|
||||
}],
|
||||
'sementity': {
|
||||
'fiction': 'nonfiction',
|
||||
'confidence': 'uncertain',
|
||||
'class': 'instance',
|
||||
'type': 'Top>Person'
|
||||
}
|
||||
}],
|
||||
'concept_list': [{
|
||||
'form':
|
||||
'world',
|
||||
'id':
|
||||
'5c053cd39d',
|
||||
'relevance':
|
||||
'100',
|
||||
'semtheme_list': [{
|
||||
'id': 'ODTHEME_ASTRONOMY',
|
||||
'type': 'Top>NaturalSciences>Astronomy'
|
||||
}]
|
||||
}],
|
||||
}
|
||||
}]
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from senpy import easy_test
|
||||
easy_test()
|
||||
@ -1,42 +0,0 @@
|
||||
# Sentimet-vader plugin
|
||||
|
||||
Vader is a plugin developed at GSI UPM for sentiment analysis.
|
||||
The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web.
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
This plugin uses the vaderSentiment module underneath, which is described in the paper:
|
||||
|
||||
VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text
|
||||
C.J. Hutto and Eric Gilbert
|
||||
Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
|
||||
|
||||
If you use this plugin in your research, please cite the above paper.
|
||||
|
||||
For more information about the functionality, check the official repository
|
||||
|
||||
https://github.com/cjhutto/vaderSentiment
|
||||
|
||||
## Usage
|
||||
|
||||
Parameters:
|
||||
|
||||
- Language: es (Spanish), en(English).
|
||||
- Input: Text to analyse.
|
||||
|
||||
|
||||
Example request:
|
||||
|
||||
```
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=sentiment-vader&language=en&input=I%20love%20Madrid
|
||||
```
|
||||
|
||||
Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section.
|
||||
|
||||
This plugin supports **python3**
|
||||
|
||||
![alt GSI Logo][logoGSI]
|
||||
|
||||
[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"
|
||||
|
||||
========
|
||||
@ -1,368 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# coding: utf-8
|
||||
'''
|
||||
Created on July 04, 2013
|
||||
@author: C.J. Hutto
|
||||
|
||||
Citation Information
|
||||
|
||||
If you use any of the VADER sentiment analysis tools
|
||||
(VADER sentiment lexicon or Python code for rule-based sentiment
|
||||
analysis engine) in your work or research, please cite the paper.
|
||||
For example:
|
||||
|
||||
Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for
|
||||
Sentiment Analysis of Social Media Text. Eighth International Conference on
|
||||
Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
|
||||
'''
|
||||
|
||||
import os, math, re, sys, fnmatch, string
|
||||
import codecs
|
||||
|
||||
def make_lex_dict(f):
|
||||
maps = {}
|
||||
with codecs.open(f, encoding='iso-8859-1') as f:
|
||||
for wmsr in f:
|
||||
w, m = wmsr.strip().split('\t')[:2]
|
||||
maps[w] = m
|
||||
return maps
|
||||
|
||||
f = 'vader_sentiment_lexicon.txt' # empirically derived valence ratings for words, emoticons, slang, swear words, acronyms/initialisms
|
||||
try:
|
||||
word_valence_dict = make_lex_dict(f)
|
||||
except:
|
||||
f = os.path.join(os.path.dirname(__file__),'vader_sentiment_lexicon.txt')
|
||||
word_valence_dict = make_lex_dict(f)
|
||||
|
||||
# for removing punctuation
|
||||
regex_remove_punctuation = re.compile('[%s]' % re.escape(string.punctuation))
|
||||
|
||||
def sentiment(text):
|
||||
"""
|
||||
Returns a float for sentiment strength based on the input text.
|
||||
Positive values are positive valence, negative value are negative valence.
|
||||
"""
|
||||
wordsAndEmoticons = str(text).split() #doesn't separate words from adjacent punctuation (keeps emoticons & contractions)
|
||||
text_mod = regex_remove_punctuation.sub('', text) # removes punctuation (but loses emoticons & contractions)
|
||||
wordsOnly = str(text_mod).split()
|
||||
# get rid of empty items or single letter "words" like 'a' and 'I' from wordsOnly
|
||||
for word in wordsOnly:
|
||||
if len(word) <= 1:
|
||||
wordsOnly.remove(word)
|
||||
# now remove adjacent & redundant punctuation from [wordsAndEmoticons] while keeping emoticons and contractions
|
||||
puncList = [".", "!", "?", ",", ";", ":", "-", "'", "\"",
|
||||
"!!", "!!!", "??", "???", "?!?", "!?!", "?!?!", "!?!?"]
|
||||
for word in wordsOnly:
|
||||
for p in puncList:
|
||||
pword = p + word
|
||||
x1 = wordsAndEmoticons.count(pword)
|
||||
while x1 > 0:
|
||||
i = wordsAndEmoticons.index(pword)
|
||||
wordsAndEmoticons.remove(pword)
|
||||
wordsAndEmoticons.insert(i, word)
|
||||
x1 = wordsAndEmoticons.count(pword)
|
||||
|
||||
wordp = word + p
|
||||
x2 = wordsAndEmoticons.count(wordp)
|
||||
while x2 > 0:
|
||||
i = wordsAndEmoticons.index(wordp)
|
||||
wordsAndEmoticons.remove(wordp)
|
||||
wordsAndEmoticons.insert(i, word)
|
||||
x2 = wordsAndEmoticons.count(wordp)
|
||||
# get rid of residual empty items or single letter "words" like 'a' and 'I' from wordsAndEmoticons
|
||||
for word in wordsAndEmoticons:
|
||||
if len(word) <= 1:
|
||||
wordsAndEmoticons.remove(word)
|
||||
|
||||
# remove stopwords from [wordsAndEmoticons]
|
||||
#stopwords = [str(word).strip() for word in open('stopwords.txt')]
|
||||
#for word in wordsAndEmoticons:
|
||||
# if word in stopwords:
|
||||
# wordsAndEmoticons.remove(word)
|
||||
|
||||
# check for negation
|
||||
negate = ["aint", "arent", "cannot", "cant", "couldnt", "darent", "didnt", "doesnt",
|
||||
"ain't", "aren't", "can't", "couldn't", "daren't", "didn't", "doesn't",
|
||||
"dont", "hadnt", "hasnt", "havent", "isnt", "mightnt", "mustnt", "neither",
|
||||
"don't", "hadn't", "hasn't", "haven't", "isn't", "mightn't", "mustn't",
|
||||
"neednt", "needn't", "never", "none", "nope", "nor", "not", "nothing", "nowhere",
|
||||
"oughtnt", "shant", "shouldnt", "uhuh", "wasnt", "werent",
|
||||
"oughtn't", "shan't", "shouldn't", "uh-uh", "wasn't", "weren't",
|
||||
"without", "wont", "wouldnt", "won't", "wouldn't", "rarely", "seldom", "despite"]
|
||||
def negated(list, nWords=[], includeNT=True):
|
||||
nWords.extend(negate)
|
||||
for word in nWords:
|
||||
if word in list:
|
||||
return True
|
||||
if includeNT:
|
||||
for word in list:
|
||||
if "n't" in word:
|
||||
return True
|
||||
if "least" in list:
|
||||
i = list.index("least")
|
||||
if i > 0 and list[i-1] != "at":
|
||||
return True
|
||||
return False
|
||||
|
||||
def normalize(score, alpha=15):
|
||||
# normalize the score to be between -1 and 1 using an alpha that approximates the max expected value
|
||||
normScore = score/math.sqrt( ((score*score) + alpha) )
|
||||
return normScore
|
||||
|
||||
def wildCardMatch(patternWithWildcard, listOfStringsToMatchAgainst):
|
||||
listOfMatches = fnmatch.filter(listOfStringsToMatchAgainst, patternWithWildcard)
|
||||
return listOfMatches
|
||||
|
||||
|
||||
def isALLCAP_differential(wordList):
|
||||
countALLCAPS= 0
|
||||
for w in wordList:
|
||||
if str(w).isupper():
|
||||
countALLCAPS += 1
|
||||
cap_differential = len(wordList) - countALLCAPS
|
||||
if cap_differential > 0 and cap_differential < len(wordList):
|
||||
isDiff = True
|
||||
else: isDiff = False
|
||||
return isDiff
|
||||
isCap_diff = isALLCAP_differential(wordsAndEmoticons)
|
||||
|
||||
b_incr = 0.293 #(empirically derived mean sentiment intensity rating increase for booster words)
|
||||
b_decr = -0.293
|
||||
# booster/dampener 'intensifiers' or 'degree adverbs' http://en.wiktionary.org/wiki/Category:English_degree_adverbs
|
||||
booster_dict = {"absolutely": b_incr, "amazingly": b_incr, "awfully": b_incr, "completely": b_incr, "considerably": b_incr,
|
||||
"decidedly": b_incr, "deeply": b_incr, "effing": b_incr, "enormously": b_incr,
|
||||
"entirely": b_incr, "especially": b_incr, "exceptionally": b_incr, "extremely": b_incr,
|
||||
"fabulously": b_incr, "flipping": b_incr, "flippin": b_incr,
|
||||
"fricking": b_incr, "frickin": b_incr, "frigging": b_incr, "friggin": b_incr, "fully": b_incr, "fucking": b_incr,
|
||||
"greatly": b_incr, "hella": b_incr, "highly": b_incr, "hugely": b_incr, "incredibly": b_incr,
|
||||
"intensely": b_incr, "majorly": b_incr, "more": b_incr, "most": b_incr, "particularly": b_incr,
|
||||
"purely": b_incr, "quite": b_incr, "really": b_incr, "remarkably": b_incr,
|
||||
"so": b_incr, "substantially": b_incr,
|
||||
"thoroughly": b_incr, "totally": b_incr, "tremendously": b_incr,
|
||||
"uber": b_incr, "unbelievably": b_incr, "unusually": b_incr, "utterly": b_incr,
|
||||
"very": b_incr,
|
||||
|
||||
"almost": b_decr, "barely": b_decr, "hardly": b_decr, "just enough": b_decr,
|
||||
"kind of": b_decr, "kinda": b_decr, "kindof": b_decr, "kind-of": b_decr,
|
||||
"less": b_decr, "little": b_decr, "marginally": b_decr, "occasionally": b_decr, "partly": b_decr,
|
||||
"scarcely": b_decr, "slightly": b_decr, "somewhat": b_decr,
|
||||
"sort of": b_decr, "sorta": b_decr, "sortof": b_decr, "sort-of": b_decr}
|
||||
sentiments = []
|
||||
for item in wordsAndEmoticons:
|
||||
v = 0
|
||||
i = wordsAndEmoticons.index(item)
|
||||
if (i < len(wordsAndEmoticons)-1 and str(item).lower() == "kind" and \
|
||||
str(wordsAndEmoticons[i+1]).lower() == "of") or str(item).lower() in booster_dict:
|
||||
sentiments.append(v)
|
||||
continue
|
||||
item_lowercase = str(item).lower()
|
||||
if item_lowercase in word_valence_dict:
|
||||
#get the sentiment valence
|
||||
v = float(word_valence_dict[item_lowercase])
|
||||
|
||||
#check if sentiment laden word is in ALLCAPS (while others aren't)
|
||||
c_incr = 0.733 #(empirically derived mean sentiment intensity rating increase for using ALLCAPs to emphasize a word)
|
||||
if str(item).isupper() and isCap_diff:
|
||||
if v > 0: v += c_incr
|
||||
else: v -= c_incr
|
||||
|
||||
#check if the preceding words increase, decrease, or negate/nullify the valence
|
||||
def scalar_inc_dec(word, valence):
|
||||
scalar = 0.0
|
||||
word_lower = str(word).lower()
|
||||
if word_lower in booster_dict:
|
||||
scalar = booster_dict[word_lower]
|
||||
if valence < 0: scalar *= -1
|
||||
#check if booster/dampener word is in ALLCAPS (while others aren't)
|
||||
if str(word).isupper() and isCap_diff:
|
||||
if valence > 0: scalar += c_incr
|
||||
else: scalar -= c_incr
|
||||
return scalar
|
||||
n_scalar = -0.74
|
||||
if i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict:
|
||||
s1 = scalar_inc_dec(wordsAndEmoticons[i-1], v)
|
||||
v = v+s1
|
||||
if negated([wordsAndEmoticons[i-1]]): v = v*n_scalar
|
||||
if i > 1 and str(wordsAndEmoticons[i-2]).lower() not in word_valence_dict:
|
||||
s2 = scalar_inc_dec(wordsAndEmoticons[i-2], v)
|
||||
if s2 != 0: s2 = s2*0.95
|
||||
v = v+s2
|
||||
# check for special use of 'never' as valence modifier instead of negation
|
||||
if wordsAndEmoticons[i-2] == "never" and (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"):
|
||||
v = v*1.5
|
||||
# otherwise, check for negation/nullification
|
||||
elif negated([wordsAndEmoticons[i-2]]): v = v*n_scalar
|
||||
if i > 2 and str(wordsAndEmoticons[i-3]).lower() not in word_valence_dict:
|
||||
s3 = scalar_inc_dec(wordsAndEmoticons[i-3], v)
|
||||
if s3 != 0: s3 = s3*0.9
|
||||
v = v+s3
|
||||
# check for special use of 'never' as valence modifier instead of negation
|
||||
if wordsAndEmoticons[i-3] == "never" and \
|
||||
(wordsAndEmoticons[i-2] == "so" or wordsAndEmoticons[i-2] == "this") or \
|
||||
(wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"):
|
||||
v = v*1.25
|
||||
# otherwise, check for negation/nullification
|
||||
elif negated([wordsAndEmoticons[i-3]]): v = v*n_scalar
|
||||
|
||||
# check for special case idioms using a sentiment-laden keyword known to SAGE
|
||||
special_case_idioms = {"the shit": 3, "the bomb": 3, "bad ass": 1.5, "yeah right": -2,
|
||||
"cut the mustard": 2, "kiss of death": -1.5, "hand to mouth": -2}
|
||||
# future work: consider other sentiment-laden idioms
|
||||
#other_idioms = {"back handed": -2, "blow smoke": -2, "blowing smoke": -2, "upper hand": 1, "break a leg": 2,
|
||||
# "cooking with gas": 2, "in the black": 2, "in the red": -2, "on the ball": 2,"under the weather": -2}
|
||||
onezero = "{} {}".format(str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i]))
|
||||
twoonezero = "{} {} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i]))
|
||||
twoone = "{} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]))
|
||||
threetwoone = "{} {} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]))
|
||||
threetwo = "{} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2]))
|
||||
if onezero in special_case_idioms: v = special_case_idioms[onezero]
|
||||
elif twoonezero in special_case_idioms: v = special_case_idioms[twoonezero]
|
||||
elif twoone in special_case_idioms: v = special_case_idioms[twoone]
|
||||
elif threetwoone in special_case_idioms: v = special_case_idioms[threetwoone]
|
||||
elif threetwo in special_case_idioms: v = special_case_idioms[threetwo]
|
||||
if len(wordsAndEmoticons)-1 > i:
|
||||
zeroone = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1]))
|
||||
if zeroone in special_case_idioms: v = special_case_idioms[zeroone]
|
||||
if len(wordsAndEmoticons)-1 > i+1:
|
||||
zeroonetwo = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1]), str(wordsAndEmoticons[i+2]))
|
||||
if zeroonetwo in special_case_idioms: v = special_case_idioms[zeroonetwo]
|
||||
|
||||
# check for booster/dampener bi-grams such as 'sort of' or 'kind of'
|
||||
if threetwo in booster_dict or twoone in booster_dict:
|
||||
v = v+b_decr
|
||||
|
||||
# check for negation case using "least"
|
||||
if i > 1 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \
|
||||
and str(wordsAndEmoticons[i-1]).lower() == "least":
|
||||
if (str(wordsAndEmoticons[i-2]).lower() != "at" and str(wordsAndEmoticons[i-2]).lower() != "very"):
|
||||
v = v*n_scalar
|
||||
elif i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \
|
||||
and str(wordsAndEmoticons[i-1]).lower() == "least":
|
||||
v = v*n_scalar
|
||||
sentiments.append(v)
|
||||
|
||||
# check for modification in sentiment due to contrastive conjunction 'but'
|
||||
if 'but' in wordsAndEmoticons or 'BUT' in wordsAndEmoticons:
|
||||
try: bi = wordsAndEmoticons.index('but')
|
||||
except: bi = wordsAndEmoticons.index('BUT')
|
||||
for s in sentiments:
|
||||
si = sentiments.index(s)
|
||||
if si < bi:
|
||||
sentiments.pop(si)
|
||||
sentiments.insert(si, s*0.5)
|
||||
elif si > bi:
|
||||
sentiments.pop(si)
|
||||
sentiments.insert(si, s*1.5)
|
||||
|
||||
if sentiments:
|
||||
sum_s = float(sum(sentiments))
|
||||
#print sentiments, sum_s
|
||||
|
||||
# check for added emphasis resulting from exclamation points (up to 4 of them)
|
||||
ep_count = str(text).count("!")
|
||||
if ep_count > 4: ep_count = 4
|
||||
ep_amplifier = ep_count*0.292 #(empirically derived mean sentiment intensity rating increase for exclamation points)
|
||||
if sum_s > 0: sum_s += ep_amplifier
|
||||
elif sum_s < 0: sum_s -= ep_amplifier
|
||||
|
||||
# check for added emphasis resulting from question marks (2 or 3+)
|
||||
qm_count = str(text).count("?")
|
||||
qm_amplifier = 0
|
||||
if qm_count > 1:
|
||||
if qm_count <= 3: qm_amplifier = qm_count*0.18
|
||||
else: qm_amplifier = 0.96
|
||||
if sum_s > 0: sum_s += qm_amplifier
|
||||
elif sum_s < 0: sum_s -= qm_amplifier
|
||||
|
||||
compound = normalize(sum_s)
|
||||
|
||||
# want separate positive versus negative sentiment scores
|
||||
pos_sum = 0.0
|
||||
neg_sum = 0.0
|
||||
neu_count = 0
|
||||
for sentiment_score in sentiments:
|
||||
if sentiment_score > 0:
|
||||
pos_sum += (float(sentiment_score) +1) # compensates for neutral words that are counted as 1
|
||||
if sentiment_score < 0:
|
||||
neg_sum += (float(sentiment_score) -1) # when used with math.fabs(), compensates for neutrals
|
||||
if sentiment_score == 0:
|
||||
neu_count += 1
|
||||
|
||||
if pos_sum > math.fabs(neg_sum): pos_sum += (ep_amplifier+qm_amplifier)
|
||||
elif pos_sum < math.fabs(neg_sum): neg_sum -= (ep_amplifier+qm_amplifier)
|
||||
|
||||
total = pos_sum + math.fabs(neg_sum) + neu_count
|
||||
pos = math.fabs(pos_sum / total)
|
||||
neg = math.fabs(neg_sum / total)
|
||||
neu = math.fabs(neu_count / total)
|
||||
|
||||
else:
|
||||
compound = 0.0; pos = 0.0; neg = 0.0; neu = 0.0
|
||||
|
||||
s = {"neg" : round(neg, 3),
|
||||
"neu" : round(neu, 3),
|
||||
"pos" : round(pos, 3),
|
||||
"compound" : round(compound, 4)}
|
||||
return s
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# --- examples -------
|
||||
sentences = [
|
||||
"VADER is smart, handsome, and funny.", # positive sentence example
|
||||
"VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted)
|
||||
"VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted)
|
||||
"VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled
|
||||
"VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity
|
||||
"VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score
|
||||
"The book was good.", # positive sentence
|
||||
"The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted)
|
||||
"The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence
|
||||
"A really bad, horrible book.", # negative sentence with booster words
|
||||
"At least it isn't a horrible book.", # negated negative sentence with contraction
|
||||
":) and :D", # emoticons handled
|
||||
"", # an empty string is correctly handled
|
||||
"Today sux", # negative slang handled
|
||||
"Today sux!", # negative slang with punctuation emphasis handled
|
||||
"Today SUX!", # negative slang with capitalization emphasis
|
||||
"Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but"
|
||||
]
|
||||
paragraph = "It was one of the worst movies I've seen, despite good reviews. \
|
||||
Unbelievably bad acting!! Poor direction. VERY poor production. \
|
||||
The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!"
|
||||
|
||||
from nltk import tokenize
|
||||
lines_list = tokenize.sent_tokenize(paragraph)
|
||||
sentences.extend(lines_list)
|
||||
|
||||
tricky_sentences = [
|
||||
"Most automated sentiment analysis tools are shit.",
|
||||
"VADER sentiment analysis is the shit.",
|
||||
"Sentiment analysis has never been good.",
|
||||
"Sentiment analysis with VADER has never been this good.",
|
||||
"Warren Beatty has never been so entertaining.",
|
||||
"I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either.",
|
||||
"I like to hate Michael Bay films, but I couldn't fault this one",
|
||||
"It's one thing to watch an Uwe Boll film, but another thing entirely to pay for it",
|
||||
"The movie was too good",
|
||||
"This movie was actually neither that funny, nor super witty.",
|
||||
"This movie doesn't care about cleverness, wit or any other kind of intelligent humor.",
|
||||
"Those who find ugly meanings in beautiful things are corrupt without being charming.",
|
||||
"There are slow and repetitive parts, BUT it has just enough spice to keep it interesting.",
|
||||
"The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT!",
|
||||
"Roger Dodger is one of the most compelling variations on this theme.",
|
||||
"Roger Dodger is one of the least compelling variations on this theme.",
|
||||
"Roger Dodger is at least compelling as a variation on the theme.",
|
||||
"they fall in love with the product",
|
||||
"but then it breaks",
|
||||
"usually around the time the 90 day warranty expires",
|
||||
"the twin towers collapsed today",
|
||||
"However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ''least offensive way possible.''"
|
||||
]
|
||||
sentences.extend(tricky_sentences)
|
||||
for sentence in sentences:
|
||||
print(sentence)
|
||||
ss = sentiment(sentence)
|
||||
print("\t" + str(ss))
|
||||
|
||||
print("\n\n Done!")
|
||||
@ -1,74 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from vaderSentiment import sentiment
|
||||
from senpy.plugins import SentimentBox, SenpyPlugin
|
||||
from senpy.models import Results, Sentiment, Entry
|
||||
import logging
|
||||
|
||||
|
||||
class VaderSentimentPlugin(SentimentBox):
|
||||
'''
|
||||
Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.
|
||||
'''
|
||||
name = "sentiment-vader"
|
||||
module = "sentiment-vader"
|
||||
author = "@icorcuera"
|
||||
version = "0.1.1"
|
||||
extra_params = {
|
||||
"language": {
|
||||
"description": "language of the input",
|
||||
"@id": "lang_rand",
|
||||
"aliases": ["language", "l"],
|
||||
"default": "auto",
|
||||
"options": ["es", "en", "auto"]
|
||||
},
|
||||
|
||||
"aggregate": {
|
||||
"description": "Show only the strongest sentiment (aggregate) or all sentiments",
|
||||
"aliases": ["aggregate","agg"],
|
||||
"options": [True, False],
|
||||
"default": False
|
||||
}
|
||||
|
||||
}
|
||||
requirements = {}
|
||||
|
||||
_VADER_KEYS = ['pos', 'neu', 'neg']
|
||||
binary = False
|
||||
|
||||
|
||||
def predict_one(self, features, activity):
|
||||
text_input = ' '.join(features)
|
||||
scores = sentiment(text_input)
|
||||
|
||||
sentiments = []
|
||||
for k in self._VADER_KEYS:
|
||||
sentiments.append(scores[k])
|
||||
|
||||
if activity.param('aggregate'):
|
||||
m = max(sentiments)
|
||||
sentiments = [k if k==m else None for k in sentiments]
|
||||
|
||||
return sentiments
|
||||
|
||||
test_cases = []
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'input': 'I am tired :(',
|
||||
'polarity': 'marl:Negative'
|
||||
},
|
||||
{
|
||||
'input': 'I love pizza :(',
|
||||
'polarity': 'marl:Positive'
|
||||
},
|
||||
{
|
||||
'input': 'I enjoy going to the cinema :)',
|
||||
'polarity': 'marl:Negative'
|
||||
},
|
||||
{
|
||||
'input': 'This cake is disgusting',
|
||||
'polarity': 'marl:Negative'
|
||||
},
|
||||
|
||||
]
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue