mirror of
https://github.com/gsi-upm/senpy
synced 2024-11-14 12:42:27 +00:00
261 lines
9.2 KiB
Python
261 lines
9.2 KiB
Python
# -*- 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 SentimentPlugin, SenpyPlugin
|
|
from senpy.models import Results, EmotionSet, Entry, Emotion
|
|
|
|
|
|
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):
|
|
self._stopwords = stopwords.words('english')
|
|
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):
|
|
|
|
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 = parse_es(text,lemmata=True).split()
|
|
else:
|
|
sentences = 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 _emotion_calculate(self, VAD):
|
|
emotion=''
|
|
value=10000000000000000000000.0
|
|
for state in self.centroids:
|
|
valence=VAD[0]-self.centroids[state]['V']
|
|
arousal=VAD[1]-self.centroids[state]['A']
|
|
dominance=VAD[2]-self.centroids[state]['D']
|
|
new_value=math.sqrt((valence*valence)+(arousal*arousal)+(dominance*dominance))
|
|
if new_value < value:
|
|
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)
|
|
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]
|
|
emotion=self._emotion_calculate(totalVAD)
|
|
feature_set['emotion']=emotion
|
|
feature_set['V']=totalVAD[0]
|
|
feature_set['A']=totalVAD[1]
|
|
feature_set['D']=totalVAD[2]
|
|
return feature_set
|
|
|
|
def analyse_entry(self, entry, 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["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, ]
|
|
|
|
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',
|
|
}]
|
|
}]
|
|
}
|
|
}
|
|
]
|