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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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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()