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Code Issues Releases Wiki Activity

Merged into monorepo

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This commit is contained in:
J. Fernando Sánchez 2018-06-14 19:38:08 +02:00
parent e51b659030
commit c52a894017
29 changed files with 406 additions and 493 deletions
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24
.gitmodules vendored
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@ -1,15 +1,9 @@
[submodule "sentiment-meaningCloud"]
path = sentiment-meaningCloud
url = https://lab.cluster.gsi.dit.upm.es/senpy/sentiment-meaningCloud/
[submodule "sentiment-vader"]
path = sentiment-vader
url = https://lab.cluster.gsi.dit.upm.es/senpy/sentiment-vader/
[submodule "emotion-wnaffect"]
path = emotion-wnaffect
url = https://lab.cluster.gsi.dit.upm.es/senpy/emotion-wnaffect
[submodule "emotion-anew"]
path = emotion-anew
url = https://lab.cluster.gsi.dit.upm.es/senpy/emotion-anew
[submodule "sentiment-basic"]
path = sentiment-basic
url = https://lab.cluster.gsi.dit.upm.es/senpy/sentiment-basic
[submodule "emotion-anew/data"]
path = emotion-anew/data
url = https://lab.cluster.gsi.dit.upm.es/senpy/data/emotion-anew.git
[submodule "emotion-wnaffect/data"]
path = emotion-wnaffect/data
url = https://lab.cluster.gsi.dit.upm.es/senpy/data/emotion-wnaffect.git
[submodule "sentiment-basic/data"]
path = sentiment-basic/data
url = https://lab.cluster.gsi.dit.upm.es/senpy/data/sentiment-basic.git

3
Dockerfile
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@ -1,9 +1,10 @@
from gsiupm/senpy:0.8.7-python2.7
from gsiupm/senpy:0.10.5-python2.7
RUN python -m nltk.downloader stopwords
RUN python -m nltk.downloader punkt
RUN python -m nltk.downloader maxent_treebank_pos_tagger
RUN python -m nltk.downloader wordnet
RUN python -m nltk.downloader omw
ADD . /senpy-plugins

11
Makefile
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@ -3,6 +3,11 @@ NAME=senpycommunity
REPO=gsiupm
VERSION=test
PLUGINS= $(filter %/, $(wildcard */))
DOCKER_FLAGS=
ifdef SENPY_FOLDER
DOCKER_FLAGS+=-v $(realpath $(SENPY_FOLDER)):/usr/src/app/
endif
all: build run
@ -11,15 +16,15 @@ build: clean Dockerfile
docker build -t '$(REPO)/$(NAME):$(VERSION)-python$(PYVERSION)' -f Dockerfile .;
test-%:
docker run -v $$PWD/$*:/senpy-plugins/ --rm --entrypoint=/usr/local/bin/py.test -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYVERSION)' test.py
docker run $(DOCKER_FLAGS) -v $$PWD/$*:/senpy-plugins/ --rm -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYVERSION)' --only-test $(TEST_FLAGS)
test: $(addprefix test-,$(PLUGINS))
test: test-.
clean:
@docker ps -a | awk '/$(REPO)\/$(NAME)/{ split($$2, vers, "-"); if(vers[1] != "${VERSION}"){ print $$1;}}' | xargs docker rm 2>/dev/null|| true
@docker images | awk '/$(REPO)\/$(NAME)/{ split($$2, vers, "-"); if(vers[1] != "${VERSION}"){ print $$1":"$$2;}}' | xargs docker rmi 2>/dev/null|| true
run: build
docker run --rm -p 5000:5000 -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYMAIN)'
docker run $(DOCKER_FLAGS) --rm -p 5000:5000 -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYMAIN)'
.PHONY: test test-% build-% build test test_pip run clean

3
emotion-anew/.gitmodules vendored
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@ -1,3 +0,0 @@
[submodule "data"]
path = data
url = ../data/emotion-anew

162
emotion-anew/emotion-anew.py
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@ -23,12 +23,81 @@ from senpy.plugins import SentimentPlugin, SenpyPlugin
from senpy.models import Results, EmotionSet, Entry, Emotion
class EmotionTextPlugin(SentimentPlugin):
class ANEW(SentimentPlugin):
description = "This plugin consists on an emotion classifier using ANEW lexicon dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. 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."
author = "@icorcuera"
version = "0.5.1"
name = "emotion-anew"
extra_params = {
"language": {
"aliases": ["language", "l"],
"required": True,
"options": ["es","en"],
"default": "en"
}
}
anew_path_es = "Dictionary/Redondo(2007).csv"
anew_path_en = "Dictionary/ANEW2010All.txt"
centroids = {
"anger": {
"A": 6.95,
"D": 5.1,
"V": 2.7
},
"disgust": {
"A": 5.3,
"D": 8.05,
"V": 2.7
},
"fear": {
"A": 6.5,
"D": 3.6,
"V": 3.2
},
"joy": {
"A": 7.22,
"D": 6.28,
"V": 8.6
},
"sadness": {
"A": 5.21,
"D": 2.82,
"V": 2.21
}
}
emotions_ontology = {
"anger": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#anger",
"disgust": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#disgust",
"fear": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#negative-fear",
"joy": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#joy",
"neutral": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#neutral-emotion",
"sadness": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#sadness"
}
onyx__usesEmotionModel = "emoml:big6"
nltk_resources = ['stopwords']
def activate(self, *args, **kwargs):
nltk.download('stopwords')
self._stopwords = stopwords.words('english')
self._local_path=os.path.dirname(os.path.abspath(__file__))
dictionary={}
dictionary['es'] = {}
with self.open(self.anew_path_es,'rb') 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,'rb') 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):
@ -63,8 +132,8 @@ class EmotionTextPlugin(SentimentPlugin):
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])
unigrams_lemmas.append(token[4])
pos_tagged.append(token[1])
return unigrams_lemmas,unigrams_words,pos_tagged
@ -83,7 +152,7 @@ class EmotionTextPlugin(SentimentPlugin):
value=new_value
emotion=state
return emotion
def _extract_features(self, tweet,dictionary,lang):
feature_set={}
ngrams_lemmas,ngrams_words,pos_tagged = self._extract_ngrams(tweet,lang)
@ -116,41 +185,76 @@ class EmotionTextPlugin(SentimentPlugin):
def analyse_entry(self, entry, params):
text_input = entry.get("text", None)
text_input = entry.text
text= self._my_preprocessor(text_input)
dictionary={}
lang = params.get("language", "auto")
if lang == 'es':
with open(self._local_path + self.anew_path_es,'rb') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
dictionary[row[2]]={}
dictionary[row[2]]['V']=row[3]
dictionary[row[2]]['A']=row[5]
dictionary[row[2]]['D']=row[7]
else:
with open(self._local_path + self.anew_path_en,'rb') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
dictionary[row[0]]={}
dictionary[row[0]]['V']=row[2]
dictionary[row[0]]['A']=row[4]
dictionary[row[0]]['D']=row[6]
text = self._my_preprocessor(text_input)
dictionary = self._dictionary[params['language']]
feature_set=self._extract_features(text,dictionary,lang)
feature_set=self._extract_features(text, dictionary, params['language'])
emotions = EmotionSet()
emotions.id = "Emotions0"
emotion1 = Emotion(id="Emotion0")
emotion1["onyx:hasEmotionCategory"] = self.emotions_ontology[feature_set['emotion']]
emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence"] = feature_set['V']
emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal"] = feature_set['A']
emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance"] = feature_set['D']
emotion1.prov(self)
emotions.prov(self)
emotions.onyx__hasEmotion.append(emotion1)
entry.emotions = [emotions,]
entry.emotions = [emotions, ]
yield entry
ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
test_cases = [
{
'input': 'I hate you',
'expected': {
'emotions': [{
'onyx:hasEmotion': [{
'onyx:hasEmotionCategory': ontology + 'anger',
}]
}]
}
}, {
'input': 'i am sad',
'expected': {
'emotions': [{
'onyx:hasEmotion': [{
'onyx:hasEmotionCategory': ontology + 'sadness',
}]
}]
}
}, {
'input': 'i am happy with my marks',
'expected': {
'emotions': [{
'onyx:hasEmotion': [{
'onyx:hasEmotionCategory': ontology + 'joy',
}]
}]
}
}, {
'input': 'This movie is scary',
'expected': {
'emotions': [{
'onyx:hasEmotion': [{
'onyx:hasEmotionCategory': ontology + 'negative-fear',
}]
}]
}
}, {
'input': 'this cake is disgusting' ,
'expected': {
'emotions': [{
'onyx:hasEmotion': [{
'onyx:hasEmotionCategory': ontology + 'negative-fear',
}]
}]
}
}
]

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emotion-anew/emotion-anew.pyc
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76
emotion-anew/emotion-anew.senpy
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@ -1,64 +1,12 @@
{
"name": "emotion-anew",
"module": "emotion-anew",
"description": "This plugin consists on an emotion classifier using ANEW lexicon dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. 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.",
"author": "@icorcuera",
"version": "0.5",
"extra_params": {
"language": {
"aliases": ["language", "l"],
"required": true,
"options": ["es","en"],
"default": "en"
}
},
"requirements": {},
"anew_path_es": "/data/Dictionary/Redondo(2007).csv",
"anew_path_en": "/data/Dictionary/ANEW2010All.txt",
"centroids": {
"anger": {
"A": 6.95,
"D": 5.1,
"V": 2.7
},
"disgust": {
"A": 5.3,
"D": 8.05,
"V": 2.7
},
"fear": {
"A": 6.5,
"D": 3.6,
"V": 3.2
},
"joy": {
"A": 7.22,
"D": 6.28,
"V": 8.6
},
"sadness": {
"A": 5.21,
"D": 2.82,
"V": 2.21
}
},
"emotions_ontology": {
"anger": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#anger",
"disgust": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#disgust",
"fear": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#negative-fear",
"joy": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#joy",
"neutral": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#neutral-emotion",
"sadness": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#sadness"
},
"requirements": [
"numpy",
"pandas",
"nltk",
"scipy",
"scikit-learn",
"textblob",
"pattern",
"lxml"
],
"onyx:usesEmotionModel": "emoml:big6",
}
---
module: emotion-anew
requirements:
- numpy
- pandas
- nltk
- scipy
- scikit-learn
- textblob
- pattern
- lxml
onyx:usesEmotionModel: "emoml:big6"

45
emotion-anew/test.py
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@ -1,45 +0,0 @@
import os
import logging
logging.basicConfig()
try:
import unittest.mock as mock
except ImportError:
import mock
from senpy.extensions import Senpy
from flask import Flask
import unittest
import re
class emoTextANEWTest(unittest.TestCase):
def setUp(self):
self.app = Flask("test_plugin")
self.dir = os.path.join(os.path.dirname(__file__))
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
self.senpy.init_app(self.app)
def tearDown(self):
self.senpy.deactivate_plugin("EmoTextANEW", sync=True)
def test_analyse(self):
plugin = self.senpy.plugins["EmoTextANEW"]
plugin.activate()
ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
texts = {'I hate you': 'anger',
'i am sad': 'sadness',
'i am happy with my marks': 'joy',
'This movie is scary': 'negative-fear',
'this cake is disgusting' : 'negative-fear'}
for text in texts:
response = plugin.analyse(input=text)
expected = texts[text]
emotionSet = response.entries[0].emotions[0]
assert emotionSet['onyx:hasEmotion'][0]['onyx:hasEmotionCategory'] == ontology+expected
plugin.deactivate()
if __name__ == '__main__':
unittest.main()

3
emotion-wnaffect/.gitmodules vendored
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@ -1,3 +0,0 @@
[submodule "data"]
path = data
url = ../data/emotion-wnaffect

36
emotion-wnaffect/emotion-wnaffect.py
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@ -2,7 +2,6 @@
from __future__ import division
import re
import nltk
import logging
import os
import string
import xml.etree.ElementTree as ET
@ -14,11 +13,29 @@ from senpy.plugins import EmotionPlugin, AnalysisPlugin, ShelfMixin
from senpy.models import Results, EmotionSet, Entry, Emotion
class EmotionTextPlugin(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)
'''
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',
'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)
@ -56,7 +73,6 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
def activate(self, *args, **kwargs):
nltk.download(['stopwords', 'averaged_perceptron_tagger', 'wordnet'])
self._stopwords = stopwords.words('english')
self._wnlemma = wordnet.WordNetLemmatizer()
self._syntactics = {'N': 'n', 'V': 'v', 'J': 'a', 'S': 's', 'R': 'r'}
@ -87,16 +103,16 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
'sadness': 'sadness'
}
self._load_emotions(local_path + self.hierarchy_path)
self._load_emotions(self.find_file(self.hierarchy_path))
if 'total_synsets' not in self.sh:
total_synsets = self._load_synsets(local_path + self.synsets_path)
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(os.path.abspath("{0}".format(local_path + self._wn16_path)), nltk.data.find(local_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):

19
emotion-wnaffect/emotion-wnaffect.senpy
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@ -1,24 +1,5 @@
---
name: emotion-wnaffect
module: emotion-wnaffect
description: '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)'
author: "@icorcuera @balkian"
version: '0.2'
extra_params:
language:
"@id": lang_wnaffect
aliases:
- language
- l
required: false
options:
- en
synsets_path: "/a-synsets.xml"
hierarchy_path: "/a-hierarchy.xml"
wn16_path: "/wordnet1.6/dict"
onyx:usesEmotionModel: emoml:big6
requirements:
- nltk>=3.0.5
- lxml>=3.4.2

4
emotion-wnaffect/wnaffect.py
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@ -17,7 +17,9 @@ from nltk.corpus import WordNetCorpusReader
import xml.etree.ElementTree as ET
class WNAffect:
"""WordNet-Affect ressource."""
"""WordNet-Affect resource."""
nltk_resources = ['averaged_perceptron_tagger']
def __init__(self, wordnet16_dir, wn_domains_dir):
"""Initializes the WordNet-Affect object."""

19
example-plugin/example.py
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@ -1,19 +0,0 @@
from senpy.plugins import SentimentPlugin
from senpy.models import Response, Entry
import logging
logger = logging.getLogger(__name__)
class ExamplePlugin(SentimentPlugin):
def analyse(self, *args, **kwargs):
logger.warn('Analysing with the example.')
logger.warn('The answer to this response is: %s.' % kwargs['parameter'])
resp = Response()
ent = Entry(kwargs['input'])
ent['example:reversed'] = kwargs['input'][::-1]
ent['example:the_answer'] = kwargs['parameter']
resp.entries.append(ent)
return resp

17
example-plugin/example.senpy
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@ -1,17 +0,0 @@
{
"name": "ExamplePlugin",
"module": "example",
"description": "I am just an example",
"author": "@balkian",
"version": "0.1",
"extra_params": {
"parameter": {
"@id": "parameter",
"aliases": ["parameter", "param"],
"required": true,
"default": 42
}
},
"requirements": ["noop"],
"custom_attribute": "42"
}

37
example-plugin/example_plugin.py Normal file
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@ -0,0 +1,37 @@
from senpy.plugins import Analysis
from senpy.models import Response, Entry
import logging
logger = logging.getLogger(__name__)
class ExamplePlugin(Analysis):
'''A *VERY* simple plugin that exemplifies the development of Senpy Plugins'''
name = "example-plugin"
author = "@balkian"
version = "0.1"
extra_params = {
"parameter": {
"@id": "parameter",
"aliases": ["parameter", "param"],
"required": True,
"default": 42
}
}
custom_attribute = "42"
def analyse_entry(self, entry, params):
logger.debug('Analysing with the example.')
logger.debug('The answer to this response is: %s.' % params['parameter'])
resp = Response()
entry['example:reversed'] = entry.text[::-1]
entry['example:the_answer'] = params['parameter']
yield entry
test_cases = [{
'input': 'hello',
'expected': {
'example:reversed': 'olleh'
}
}]

23
example-plugin/test_example.py
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@ -1,23 +0,0 @@
import unittest
from flask import Flask
import os
from senpy.extensions import Senpy
class emoTextWAFTest(unittest.TestCase):
def setUp(self):
self.app = Flask("Example")
self.dir = os.path.join(os.path.dirname(__file__))
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
self.senpy.init_app(self.app)
def tearDown(self):
self.senpy.deactivate_plugin("ExamplePlugin", sync=True)
def test_analyse(self):
assert len(self.senpy.plugins.keys()) == 1
assert True
if __name__ == '__main__':
unittest.main()

3
sentiment-basic/.gitmodules vendored
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@ -1,3 +0,0 @@
[submodule "data"]
path = data
url = ../data/sentiment-basic

68
sentiment-basic/sentiment-basic.py
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@ -1,5 +1,6 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import logging
import string
import nltk
import pickle
@ -13,24 +14,40 @@ from os import path
from senpy.plugins import SentimentPlugin, SenpyPlugin
from senpy.models import Results, Entry, Sentiment
logger = logging.getLogger(__name__)
class SentiTextPlugin(SentimentPlugin):
class SentimentBasic(SentimentPlugin):
'''
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": {
"aliases": ["language", "l"],
"required": True,
"options": ["en","es", "it", "fr", "auto"],
"default": "auto"
}
}
sentiword_path = "SentiWordNet_3.0.txt"
pos_path = "unigram_spanish.pickle"
maxPolarityValue = 1
minPolarityValue = -1
nltk_resources = ['punkt','wordnet']
def _load_swn(self):
self.swn_path = path.join(path.abspath(path.dirname(__file__)), self.sentiword_path)
self.swn_path = self.find_file(self.sentiword_path)
swn = SentiWordNet(self.swn_path)
return swn
def _load_pos_tagger(self):
self.pos_path = path.join(path.abspath(path.dirname(__file__)), self.pos_path)
self.pos_path = self.find_file(self.pos_path)
with open(self.pos_path, 'r') as f:
tagger = pickle.load(f)
return tagger
def activate(self, *args, **kwargs):
nltk.download(['punkt','wordnet'])
self._swn = self._load_swn()
self._pos_tagger = self._load_pos_tagger()
@ -54,11 +71,6 @@ class SentiTextPlugin(SentimentPlugin):
tokens[i]['tokens'] = self._pos_tagger.tag(tokens[i]['tokens'])
return tokens
# def _stopwords(sentences, lang='english'):
# for i in sentences:
# sentences[i]['tokens'] = [t for t in sentences[i]['tokens'] if t not in nltk.corpus.stopwords.words(lang)]
# return sentences
def _compare_synsets(self, synsets, tokens, i):
for synset in synsets:
for word in tokens[i]['lemmas']:
@ -71,7 +83,7 @@ class SentiTextPlugin(SentimentPlugin):
def analyse_entry(self, entry, params):
language = params.get("language")
text = entry.get("text", None)
text = entry.text
tokens = self._tokenize(text)
tokens = self._pos(tokens)
sufixes = {'es':'spa','en':'eng','it':'ita','fr':'fra'}
@ -130,19 +142,41 @@ class SentiTextPlugin(SentimentPlugin):
except:
if n_pos == 0 and n_neg == 0:
g_score = 0.5
polarity = 'marl:Neutral'
polarity_value = 0
if g_score > 0.5:
if g_score >= 0.5:
polarity = 'marl:Positive'
polarity_value = 1
elif g_score < 0.5:
polarity = 'marl:Negative'
polarity_value = -1
else:
polarity = 'marl:Neutral'
polarity_value = 0
opinion = Sentiment(id="Opinion0"+'_'+str(i),
marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
opinion.prov(self)
entry.sentiments.append(opinion)
yield entry
test_cases = [
{
'input': u'Odio ir al cine',
'params': {'language': 'es'},
'polarity': 'marl:Negative'
},
{
'input': u'El cielo está nublado',
'params': {'language': 'es'},
'polarity': 'marl:Positive'
},
{
'input': u'Esta tarta está muy buena',
'params': {'language': 'es'},
'polarity': 'marl:Negative'
}
]

31
sentiment-basic/sentiment-basic.senpy
View File

@ -1,24 +1,7 @@
{
"name": "sentiment-basic",
"module": "sentiment-basic",
"description": "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.",
"author": "github.com/nachtkatze",
"version": "0.1",
"requirements": [
"nltk>=3.0.5",
"scipy>=0.14.0",
"textblob"
],
"extra_params": {
"language": {
"aliases": ["language", "l"],
"required": true,
"options": ["en","es", "it", "fr", "auto"],
"default": "auto"
},
},
"sentiword_path": "data/SentiWordNet_3.0.txt",
"pos_path": "data/unigram_spanish.pickle",
"maxPolarityValue": "1",
"minPolarityValue": "-1"
}
---
module: sentiment-basic
requirements:
- nltk>=3.0.5
- scipy>=0.14.0
- textblob

4
sentiment-basic/sentiwn.py
View File

@ -46,7 +46,7 @@ class SentiWordNet(object):
pos,syn_set_id,pos_score,neg_score,syn_set_score,\
gloss = fields
except:
print "Found data without all details"
print("Found data without all details")
pass
if pos and syn_set_score:
@ -67,4 +67,4 @@ class SentiWordNet(object):
senti_scores.append({"pos":pos_val,"neg":neg_val,\
"obj": 1.0 - (pos_val - neg_val),'synset':synset})
return senti_scores
return senti_scores

42
sentiment-basic/test.py
View File

@ -1,42 +0,0 @@
import os
import logging
logging.basicConfig()
try:
import unittest.mock as mock
except ImportError:
import mock
from senpy.extensions import Senpy
from flask import Flask
import unittest
class SentiTextTest(unittest.TestCase):
def setUp(self):
self.app = Flask("test_plugin")
self.dir = os.path.join(os.path.dirname(__file__))
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
self.senpy.init_app(self.app)
def tearDown(self):
self.senpy.deactivate_plugin("SentiText", sync=True)
def test_analyse(self):
plugin = self.senpy.plugins["SentiText"]
plugin.activate()
texts = {'Odio ir al cine' : 'marl:Neutral',
'El cielo esta nublado' : 'marl:Positive',
'Esta tarta esta muy buena' : 'marl:Neutral'}
for text in texts:
response = plugin.analyse(input=text)
sentimentSet = response.entries[0].sentiments[0]
print sentimentSet
expected = texts[text]
assert sentimentSet['marl:hasPolarity'] == expected
plugin.deactivate()
if __name__ == '__main__':
unittest.main()

99
sentiment-meaningCloud/sentiment_meaningcloud.py → sentiment-meaningCloud/meaningcloud_plugin.py
View File

@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import time
import logging
import requests
import json
import string
@ -9,20 +8,33 @@ import os
from os import path
import time
from senpy.plugins import SentimentPlugin
from senpy.models import Results, Entry, Sentiment, Error
from senpy.models import Results, Entry, Entity, Topic, Sentiment, Error
from senpy.utils import check_template
import mocked_request
from mocked_request import mocked_requests_post
try:
from unittest import mock
except ImportError:
import mock
logger = logging.getLogger(__name__)
class MeaningCloudPlugin(SentimentPlugin):
version = "0.1"
'''
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=<API key>&input=I%20love%20Madrid.
'''
name = 'sentiment-meaningcloud'
author = 'GSI UPM'
version = "1.1"
maxPolarityValue = 1
minPolarityValue = -1
extra_params = {
"language": {
@ -37,7 +49,6 @@ class MeaningCloudPlugin(SentimentPlugin):
}
}
"""MeaningCloud plugin uses API from Meaning Cloud to perform sentiment analysis."""
def _polarity(self, value):
if 'NONE' in value:
@ -81,7 +92,7 @@ class MeaningCloudPlugin(SentimentPlugin):
if not api_response.get('score_tag'):
raise Error(r.json())
entry['language_detected'] = lang
logger.info(api_response)
self.log.debug(api_response)
agg_polarity, agg_polarityValue = self._polarity(
api_response.get('score_tag', None))
agg_opinion = Sentiment(
@ -89,13 +100,14 @@ class MeaningCloudPlugin(SentimentPlugin):
marl__hasPolarity=agg_polarity,
marl__polarityValue=agg_polarityValue,
marl__opinionCount=len(api_response['sentence_list']))
agg_opinion.prov(self)
entry.sentiments.append(agg_opinion)
logger.info(api_response['sentence_list'])
self.log.debug(api_response['sentence_list'])
count = 1
for sentence in api_response['sentence_list']:
for nopinion in sentence['segment_list']:
logger.info(nopinion)
self.log.debug(nopinion)
polarity, polarityValue = self._polarity(
nopinion.get('score_tag', None))
opinion = Sentiment(
@ -107,64 +119,63 @@ class MeaningCloudPlugin(SentimentPlugin):
nif__beginIndex=nopinion.get('inip', None),
nif__endIndex=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 = Sentiment(
entity = Entity(
id="Entity{}".format(sent_entity.get('id')),
marl__describesObject="http://{}dbpedia.org/resource/{}".format(
itsrdf__taIdentRef="http://{}dbpedia.org/resource/{}".format(
mapper[lang], resource),
nif__anchorOf=sent_entity.get('form', None),
nif__beginIndex=sent_entity['variant_list'][0].get('inip', None),
nif__endIndex=sent_entity['variant_list'][0].get('endp', None))
entity[
'@type'] = "ODENTITY_{}".format(
sent_entity['sementity'].get('type', None).split(">")[-1])
sementity = sent_entity['sementity'].get('type', None).split(">")[-1]
entity['@type'] = "ODENTITY_{}".format(sementity)
entity.prov(self)
entry.entities.append(entity)
for topic in api_response_topics['concept_list']:
if 'semtheme_list' in topic:
for theme in topic['semtheme_list']:
concept = Sentiment(
id="Topic{}".format(topic.get('id')),
prov__wasDerivedFrom="http://dbpedia.org/resource/{}".
format(theme['type'].split('>')[-1]))
concept[
'@type'] = "ODTHEME_{}".format(
theme['type'].split(">")[-1])
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)
entry.topics.append(concept)
yield entry
@mock.patch('requests.post', side_effect=mocked_request.mocked_requests_post)
@mock.patch('requests.post', side_effect=mocked_requests_post)
def test(self, *args, **kwargs):
results = list()
params = {'algo': 'sentiment-meaningCloud',
'intype': 'direct',
'expanded-jsonld': 0,
'informat': 'text',
'prefix': '',
'plugin_type': 'analysisPlugin',
'urischeme': 'RFC5147String',
'outformat': 'json-ld',
'i': 'Hello World',
'input': 'Hello World',
'conversion': 'full',
params = {'algo': 'sentiment-meaningCloud',
'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',
'apikey': '00000',
'apikey': '00000',
'algorithm': 'sentiment-meaningCloud'}
for i in range(100):
res = next(self.analyse_entry(Entry(nif__isString="Hello World Obama"), params))
results.append(res.sentiments[0]['marl:hasPolarity'])
results.append(res.topics[0]['prov:wasDerivedFrom'])
results.append(res.entities[0]['prov:wasDerivedFrom'])
res = next(self.analyse_entry(Entry(nif__isString="Hello World Obama"), params))
assert 'marl:Neutral' in results
assert 'http://dbpedia.org/resource/Astronomy' in results
assert 'http://dbpedia.org/resource/Obama' in results
check_template(res,
{'sentiments': [
{'marl:hasPolarity': 'marl:Neutral'}],
'entities': [
{'itsrdf:taIdentRef': 'http://dbpedia.org/resource/Obama'}],
'topics': [
{'fam:topic-reference': 'http://dbpedia.org/resource/Astronomy'}]
})
if __name__ == '__main__':
from senpy import easy_test

1
sentiment-meaningCloud/mocked_request.py
View File

@ -7,7 +7,6 @@ def mocked_requests_post(*args, **kwargs):
def json(self):
return self.json_data
print("Mocking request")
if args[0] == 'http://api.meaningcloud.com/sentiment-2.1':
return MockResponse({
'model': 'general_en',

11
sentiment-meaningCloud/sentiment_meaningcloud.senpy
View File

@ -1,11 +0,0 @@
{
"name": "sentiment-meaningcloud",
"module": "sentiment_meaningcloud",
"description": "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=<put here your API key>&input=I%20love%20Madrid.",
"author": "GSI UPM",
"version": "1.0",
"requirements": {},
"maxPolarityValue": "1",
"minPolarityValue": "-1"
}

14
sentiment-vader/README.md
View File

@ -1,10 +1,12 @@
# 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:
For developing this plugin, it has been used the module vaderSentiment, 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.
@ -15,16 +17,16 @@ For more information about the functionality, check the official repository
https://github.com/cjhutto/vaderSentiment
The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web.
## Usage
Params accepted:
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
```

16
sentiment-vader/README.txt
View File

@ -1,16 +0,0 @@
==========
This README file describes the plugin vaderSentiment.
For developing this plugin, it has been used the module vaderSentiment, 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
========

25
sentiment-vader/sentiment-vader.senpy
View File

@ -1,25 +0,0 @@
{
"name": "sentiment-vader",
"module": "sentiment-vader",
"description": "Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.",
"author": "@icorcuera",
"version": "0.1",
"extra_params": {
"language": {
"@id": "lang_rand",
"aliases": ["language", "l"],
"required": false,
"options": ["es", "en", "auto"]
},
"aggregate": {
"aliases": ["aggregate","agg"],
"options": ["true", "false"],
"required": false,
"default": false
}
},
"requirements": {}
}

44
sentiment-vader/test.py
View File

@ -1,44 +0,0 @@
import os
import logging
logging.basicConfig()
try:
import unittest.mock as mock
except ImportError:
import mock
from senpy.extensions import Senpy
from flask import Flask
from flask.ext.testing import TestCase
import unittest
class vaderTest(unittest.TestCase):
def setUp(self):
self.app = Flask("test_plugin")
self.dir = os.path.join(os.path.dirname(__file__))
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
self.senpy.init_app(self.app)
def tearDown(self):
self.senpy.deactivate_plugin("vaderSentiment", sync=True)
def test_analyse(self):
plugin = self.senpy.plugins["vaderSentiment"]
plugin.activate()
texts = {'I am tired :(' : 'marl:Negative',
'I love pizza' : 'marl:Positive',
'I like going to the cinema :)' : 'marl:Positive',
'This cake is disgusting' : 'marl:Negative'}
for text in texts:
response = plugin.analyse(input=text)
expected = texts[text]
sentimentSet = response.entries[0].sentiments
max_sentiment = max(sentimentSet, key=lambda x: x['marl:polarityValue'])
assert max_sentiment['marl:hasPolarity'] == expected
plugin.deactivate()
if __name__ == '__main__':
unittest.main()

59
sentiment-vader/sentiment-vader.py → sentiment-vader/vader_plugin.py
View File

@ -5,15 +5,37 @@ from senpy.plugins import SentimentPlugin, SenpyPlugin
from senpy.models import Results, Sentiment, Entry
import logging
logger = logging.getLogger(__name__)
class vaderSentimentPlugin(SentimentPlugin):
class VaderSentimentPlugin(SentimentPlugin):
'''
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": {
"@id": "lang_rand",
"aliases": ["language", "l"],
"default": "auto",
"options": ["es", "en", "auto"]
},
def analyse_entry(self,entry,params):
"aggregate": {
"aliases": ["aggregate","agg"],
"options": ["true", "false"],
"default": False
}
logger.debug("Analysing with params {}".format(params))
}
requirements = {}
text_input = entry.get("text", None)
def analyse_entry(self, entry, params):
self.log.debug("Analysing with params {}".format(params))
text_input = entry.text
aggregate = params['aggregate']
score = sentiment(text_input)
@ -22,15 +44,18 @@ class vaderSentimentPlugin(SentimentPlugin):
marl__hasPolarity= "marl:Positive",
marl__algorithmConfidence= score['pos']
)
opinion0.prov(self)
opinion1 = Sentiment(id= "Opinion_negative",
marl__hasPolarity= "marl:Negative",
marl__algorithmConfidence= score['neg']
)
opinion1.prov(self)
opinion2 = Sentiment(id= "Opinion_neutral",
marl__hasPolarity = "marl:Neutral",
marl__algorithmConfidence = score['neu']
)
opinion2.prov(self)
if aggregate == 'true':
res = None
confident = max(score['neg'],score['neu'],score['pos'])
@ -47,3 +72,25 @@ class vaderSentimentPlugin(SentimentPlugin):
entry.sentiments.append(opinion2)
yield entry
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'
},
]