mirror of
https://github.com/gsi-upm/senpy
synced 2025-08-24 10:32:20 +00:00
Add 'community-plugins/' from commit '4c73797246c6aff8d055abfef73d3f0d34b933a8'
git-subtree-dir: community-plugins git-subtree-mainline:7f712952begit-subtree-split:4c73797246
This commit is contained in:
J. Fernando Sánchez
60
community-plugins/emotion-anew/README.md
Executable file
60
community-plugins/emotion-anew/README.md
Executable file
@@ -0,0 +1,60 @@
|
||||
# Plugin emotion-anew
|
||||
|
||||
This plugin consists on an **emotion classifier** that detects six possible emotions:
|
||||
- Anger : general-dislike.
|
||||
- Fear : negative-fear.
|
||||
- Disgust : shame.
|
||||
- Joy : gratitude, affective, enthusiasm, love, joy, liking.
|
||||
- Sadness : ingrattitude, daze, humlity, compassion, despair, anxiety, sadness.
|
||||
- Neutral: not detected a particulary emotion.
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
The response of this plugin uses [Onyx ontology](https://www.gsi.dit.upm.es/ontologies/onyx/) developed at GSI UPM, to express the information.
|
||||
|
||||
## Installation
|
||||
|
||||
* Download
|
||||
```
|
||||
git clone https://lab.cluster.gsi.dit.upm.es/senpy/emotion-anew.git
|
||||
```
|
||||
* Get data
|
||||
```
|
||||
cd emotion-anew
|
||||
git submodule update --init --recursive
|
||||
```
|
||||
* Run
|
||||
```
|
||||
docker run -p 5000:5000 -v $PWD:/plugins gsiupm/senpy:python2.7 -f /plugins
|
||||
```
|
||||
|
||||
## Data format
|
||||
|
||||
`data/Corpus/affective-isear.tsv` contains data from ISEAR Databank: http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615
|
||||
|
||||
##Usage
|
||||
|
||||
Params accepted:
|
||||
- Language: English (en) and Spanish (es).
|
||||
- Input: input text to analyse.
|
||||
|
||||
|
||||
Example request:
|
||||
```
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=emotion-anew&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.
|
||||
# Known issues
|
||||
|
||||
- To obtain Anew dictionary you can download from here: <https://github.com/hcorona/SMC2015/blob/master/resources/ANEW2010All.txt>
|
||||
|
||||
- This plugin only supports **Python2**
|
||||
|
||||
|
||||
![alt GSI Logo][logoGSI]
|
||||
|
||||
[logoES]: https://www.gsi.dit.upm.es/ontologies/onyx/img/eurosentiment_logo.png "EuroSentiment logo"
|
||||
[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"
|
||||
227
community-plugins/emotion-anew/emotion-anew.py
Normal file
227
community-plugins/emotion-anew/emotion-anew.py
Normal file
@@ -0,0 +1,227 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import re
|
||||
import nltk
|
||||
import csv
|
||||
import sys
|
||||
import os
|
||||
import unicodedata
|
||||
import string
|
||||
import xml.etree.ElementTree as ET
|
||||
import math
|
||||
|
||||
from sklearn.svm import LinearSVC
|
||||
from sklearn.feature_extraction import DictVectorizer
|
||||
|
||||
from nltk import bigrams
|
||||
from nltk import trigrams
|
||||
from nltk.corpus import stopwords
|
||||
|
||||
from pattern.en import parse as parse_en
|
||||
from pattern.es import parse as parse_es
|
||||
from senpy.plugins import EmotionPlugin, SenpyPlugin
|
||||
from senpy.models import Results, EmotionSet, Entry, Emotion
|
||||
|
||||
|
||||
class ANEW(EmotionPlugin):
|
||||
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`)."
|
||||
author = "@icorcuera"
|
||||
version = "0.5.2"
|
||||
name = "emotion-anew"
|
||||
|
||||
extra_params = {
|
||||
"language": {
|
||||
"description": "language of the input",
|
||||
"aliases": ["language", "l"],
|
||||
"required": True,
|
||||
"options": ["es","en"],
|
||||
"default": "en"
|
||||
}
|
||||
}
|
||||
|
||||
anew_path_es = "Dictionary/Redondo(2007).csv"
|
||||
anew_path_en = "Dictionary/ANEW2010All.txt"
|
||||
onyx__usesEmotionModel = "emoml:pad-dimensions"
|
||||
nltk_resources = ['stopwords']
|
||||
|
||||
def activate(self, *args, **kwargs):
|
||||
self._stopwords = stopwords.words('english')
|
||||
dictionary={}
|
||||
dictionary['es'] = {}
|
||||
with self.open(self.anew_path_es,'r') as tabfile:
|
||||
reader = csv.reader(tabfile, delimiter='\t')
|
||||
for row in reader:
|
||||
dictionary['es'][row[2]]={}
|
||||
dictionary['es'][row[2]]['V']=row[3]
|
||||
dictionary['es'][row[2]]['A']=row[5]
|
||||
dictionary['es'][row[2]]['D']=row[7]
|
||||
dictionary['en'] = {}
|
||||
with self.open(self.anew_path_en,'r') as tabfile:
|
||||
reader = csv.reader(tabfile, delimiter='\t')
|
||||
for row in reader:
|
||||
dictionary['en'][row[0]]={}
|
||||
dictionary['en'][row[0]]['V']=row[2]
|
||||
dictionary['en'][row[0]]['A']=row[4]
|
||||
dictionary['en'][row[0]]['D']=row[6]
|
||||
self._dictionary = dictionary
|
||||
|
||||
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, lang):
|
||||
unigrams_lemmas = []
|
||||
unigrams_words = []
|
||||
pos_tagged = []
|
||||
if lang == 'es':
|
||||
sentences = list(parse_es(text, lemmata=True).split())
|
||||
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):
|
||||
return zip(*[input_list[i:] for i in range(n)])
|
||||
|
||||
def _extract_features(self, tweet,dictionary,lang):
|
||||
feature_set={}
|
||||
ngrams_lemmas,ngrams_words,pos_tagged = self._extract_ngrams(tweet,lang)
|
||||
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["emoml:pad-dimensions_pleasure"] = feature_set['V']
|
||||
emotion1["emoml:pad-dimensions_arousal"] = feature_set['A']
|
||||
emotion1["emoml:pad-dimensions_dominance"] = feature_set['D']
|
||||
|
||||
emotion1.prov(activity)
|
||||
emotions.prov(activity)
|
||||
|
||||
emotions.onyx__hasEmotion.append(emotion1)
|
||||
entry.emotions = [emotions, ]
|
||||
|
||||
yield entry
|
||||
|
||||
ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
|
||||
test_cases = [
|
||||
{
|
||||
'name': 'anger with VAD=(2.12, 6.95, 5.05)',
|
||||
'input': 'I hate you',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 6.95,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 5.05,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 2.12,
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'input': 'i am sad',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 4.13,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 3.45,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 1.61,
|
||||
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'name': 'joy',
|
||||
'input': 'i am happy with my marks',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 6.49,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 6.63,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 8.21,
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'name': 'negative-feat',
|
||||
'input': 'This movie is scary',
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 5.8100000000000005,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 4.33,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 5.050000000000001,
|
||||
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}, {
|
||||
'name': 'negative-fear',
|
||||
'input': 'this cake is disgusting' ,
|
||||
'expected': {
|
||||
'onyx:hasEmotionSet': [{
|
||||
'onyx:hasEmotion': [{
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 5.09,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 4.4,
|
||||
"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 5.109999999999999,
|
||||
|
||||
}]
|
||||
}]
|
||||
}
|
||||
}
|
||||
]
|
||||
11
community-plugins/emotion-anew/emotion-anew.senpy
Normal file
11
community-plugins/emotion-anew/emotion-anew.senpy
Normal file
@@ -0,0 +1,11 @@
|
||||
---
|
||||
module: emotion-anew
|
||||
requirements:
|
||||
- numpy
|
||||
- pandas
|
||||
- nltk
|
||||
- scipy
|
||||
- scikit-learn
|
||||
- textblob
|
||||
- pattern
|
||||
- lxml
|
||||
Reference in New Issue
Block a user