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228 lines
9.0 KiB
Python
228 lines
9.0 KiB
Python
# -*- 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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class ANEW(EmotionPlugin):
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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. 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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extra_params = {
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"language": {
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"description": "language of the input",
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"aliases": ["language", "l"],
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"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 = "emoml:pad-dimensions"
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nltk_resources = ['stopwords']
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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]
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self._dictionary = dictionary
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def _my_preprocessor(self, text):
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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)
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text = re.sub(regAt, '', text)
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text = re.sub('RT : ', '', text)
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text = re.sub(regHttps, '', text)
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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)
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return s
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def _extract_ngrams(self, text, lang):
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unigrams_lemmas = []
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unigrams_words = []
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pos_tagged = []
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if lang == 'es':
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sentences = list(parse_es(text, lemmata=True).split())
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else:
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sentences = list(parse_en(text, lemmata=True).split())
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for sentence in sentences:
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for token in sentence:
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if token[0].lower() not in self._stopwords:
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unigrams_words.append(token[0].lower())
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unigrams_lemmas.append(token[4])
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pos_tagged.append(token[1])
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return unigrams_lemmas,unigrams_words,pos_tagged
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def _find_ngrams(self, input_list, n):
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return zip(*[input_list[i:] for i in range(n)])
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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',
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'RBS':'RB', 'VB':'VB', 'VBD':'VB', 'VGB':'VB', 'VBN':'VB', 'VBP':'VB', 'VBZ':'VB'}
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totalVAD=[0,0,0]
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matches=0
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for word in range(len(ngrams_lemmas)):
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VAD=[]
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if ngrams_lemmas[word] in dictionary:
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matches+=1
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totalVAD = [totalVAD[0]+float(dictionary[ngrams_lemmas[word]]['V']),
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totalVAD[1]+float(dictionary[ngrams_lemmas[word]]['A']),
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totalVAD[2]+float(dictionary[ngrams_lemmas[word]]['D'])]
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elif ngrams_words[word] in dictionary:
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matches+=1
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totalVAD = [totalVAD[0]+float(dictionary[ngrams_words[word]]['V']),
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totalVAD[1]+float(dictionary[ngrams_words[word]]['A']),
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totalVAD[2]+float(dictionary[ngrams_words[word]]['D'])]
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if matches==0:
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emotion='neutral'
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else:
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totalVAD=[totalVAD[0]/matches,totalVAD[1]/matches,totalVAD[2]/matches]
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feature_set['V'] = totalVAD[0]
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feature_set['A'] = totalVAD[1]
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feature_set['D'] = totalVAD[2]
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return feature_set
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def analyse_entry(self, entry, activity):
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params = activity.params
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text_input = entry.text
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text = self._my_preprocessor(text_input)
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dictionary = self._dictionary[params['language']]
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feature_set=self._extract_features(text, dictionary, params['language'])
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emotions = EmotionSet()
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emotions.id = "Emotions0"
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emotion1 = Emotion(id="Emotion0")
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emotion1["emoml:pad-dimensions_pleasure"] = feature_set['V']
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emotion1["emoml:pad-dimensions_arousal"] = feature_set['A']
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emotion1["emoml:pad-dimensions_dominance"] = feature_set['D']
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emotion1.prov(activity)
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emotions.prov(activity)
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emotions.onyx__hasEmotion.append(emotion1)
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entry.emotions = [emotions, ]
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yield entry
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ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
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test_cases = [
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{
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'name': 'anger with VAD=(2.12, 6.95, 5.05)',
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'input': 'I hate you',
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'expected': {
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 6.95,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 5.05,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 2.12,
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}]
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}]
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}
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}, {
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'input': 'i am sad',
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'expected': {
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 4.13,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 3.45,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 1.61,
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}]
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}]
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}
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}, {
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'name': 'joy',
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'input': 'i am happy with my marks',
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'expected': {
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 6.49,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 6.63,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 8.21,
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}]
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}]
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}
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}, {
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'name': 'negative-feat',
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'input': 'This movie is scary',
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'expected': {
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 5.8100000000000005,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 4.33,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 5.050000000000001,
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}]
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}]
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}
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}, {
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'name': 'negative-fear',
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'input': 'this cake is disgusting' ,
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'expected': {
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 5.09,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 4.4,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 5.109999999999999,
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}]
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}]
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}
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}
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]
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