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https://github.com/gsi-upm/senpy
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Add support for py3 in emotion-wnaffect
Normalize polarity values in sentiment-basic and sentiment-140
This commit is contained in:
militarpancho
parent
dee007eacf
commit
b671ff51f9
@ -1,5 +1,3 @@
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# -*- coding: utf-8 -*-
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from __future__ import division
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import re
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import nltk
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@ -9,32 +7,34 @@ import string
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import xml.etree.ElementTree as ET
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from nltk.corpus import stopwords
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from nltk.corpus import WordNetCorpusReader
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from nltk.stem import wordnet
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from emotion import Emotion as Emo
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from pattern.en import parse
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from senpy.plugins import EmotionPlugin, SenpyPlugin, ShelfMixin
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from senpy.plugins import EmotionPlugin, AnalysisPlugin, ShelfMixin
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from senpy.models import Results, EmotionSet, Entry, Emotion
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class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
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def _load_synsets(self, synsets_path):
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"""Returns a dictionary POS tag -> synset offset -> emotion (str -> int -> str)."""
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tree = ET.parse(synsets_path)
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root = tree.getroot()
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pos_map = { "noun": "NN", "adj": "JJ", "verb": "VB", "adv": "RB" }
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pos_map = {"noun": "NN", "adj": "JJ", "verb": "VB", "adv": "RB"}
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synsets = {}
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for pos in ["noun", "adj", "verb", "adv"]:
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tag = pos_map[pos]
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synsets[tag] = {}
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for elem in root.findall(".//{0}-syn-list//{0}-syn".format(pos, pos)):
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offset = int(elem.get("id")[2:])
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for elem in root.findall(
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".//{0}-syn-list//{0}-syn".format(pos, pos)):
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offset = int(elem.get("id")[2:])
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if not offset: continue
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if elem.get("categ"):
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synsets[tag][offset] = Emo.emotions[elem.get("categ")] if elem.get("categ") in Emo.emotions else None
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synsets[tag][offset] = Emo.emotions[elem.get(
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"categ")] if elem.get(
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"categ") in Emo.emotions else None
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elif elem.get("noun-id"):
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synsets[tag][offset] = synsets[pos_map["noun"]][int(elem.get("noun-id")[2:])]
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synsets[tag][offset] = synsets[pos_map["noun"]][int(
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elem.get("noun-id")[2:])]
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return synsets
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def _load_emotions(self, hierarchy_path):
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@ -50,45 +50,59 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
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Emo.emotions[name] = Emo(name, elem.get("isa"))
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def activate(self, *args, **kwargs):
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nltk.download('stopwords')
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nltk.download(['stopwords', 'averaged_perceptron_tagger', 'wordnet'])
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self._stopwords = stopwords.words('english')
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#local_path=os.path.dirname(os.path.abspath(__file__))
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self._categories = {'anger': ['general-dislike',],
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'fear': ['negative-fear',],
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'disgust': ['shame',],
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'joy': ['gratitude','affective','enthusiasm','love','joy','liking'],
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'sadness': ['ingrattitude','daze','humility','compassion','despair','anxiety','sadness']}
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self._wnlemma = wordnet.WordNetLemmatizer()
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self._syntactics = {'N': 'n', 'V': 'v', 'J': 'a', 'S': 's', 'R': 'r'}
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local_path = os.path.dirname(os.path.abspath(__file__))
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self._categories = {
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'anger': [
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'general-dislike',
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],
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'fear': [
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'negative-fear',
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],
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'disgust': [
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'shame',
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],
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'joy':
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['gratitude', 'affective', 'enthusiasm', 'love', 'joy', 'liking'],
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'sadness': [
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'ingrattitude', 'daze', 'humility', 'compassion', 'despair',
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'anxiety', 'sadness'
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]
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}
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self._wnaffect_mappings = {'anger': 'anger',
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'fear': 'negative-fear',
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'disgust': 'disgust',
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'joy': 'joy',
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'sadness': 'sadness'}
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self._wnaffect_mappings = {
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'anger': 'anger',
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'fear': 'negative-fear',
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'disgust': 'disgust',
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'joy': 'joy',
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'sadness': 'sadness'
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}
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self._load_emotions(local_path + self.hierarchy_path)
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self._load_emotions(self.hierarchy_path)
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if 'total_synsets' not in self.sh:
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total_synsets = self._load_synsets(self.synsets_path)
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total_synsets = self._load_synsets(local_path + self.synsets_path)
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self.sh['total_synsets'] = total_synsets
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self._total_synsets = self.sh['total_synsets']
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if 'wn16' not in self.sh:
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self._wn16_path = self.wn16_path
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wn16 = WordNetCorpusReader(os.path.abspath("{0}".format(self._wn16_path)), nltk.data.find(self._wn16_path))
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self.sh['wn16'] = wn16
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self._wn16 = self.sh['wn16']
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self._wn16_path = self.wn16_path
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self._wn16 = WordNetCorpusReader(os.path.abspath("{0}".format(local_path + self._wn16_path)), nltk.data.find(local_path + self._wn16_path))
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def deactivate(self, *args, **kwargs):
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self.save()
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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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regHttp = re.compile(
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'(http://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
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regHttps = re.compile(
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'(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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@ -109,56 +123,82 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
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unigrams_lemmas = []
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pos_tagged = []
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unigrams_words = []
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sentences = parse(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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tokens = text.split()
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for token in nltk.pos_tag(tokens):
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unigrams_words.append(token[0])
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pos_tagged.append(token[1])
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if token[1][0] in self._syntactics.keys():
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unigrams_lemmas.append(
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self._wnlemma.lemmatize(token[0], self._syntactics[token[1]
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[0]]))
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else:
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unigrams_lemmas.append(token[0])
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return unigrams_words,unigrams_lemmas,pos_tagged
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return unigrams_words, unigrams_lemmas, 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 _clean_pos(self, pos_tagged):
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pos_tags={'NN':'NN', 'NNP':'NN','NNP-LOC':'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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pos_tags = {
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'NN': 'NN',
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'NNP': 'NN',
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'NNP-LOC': 'NN',
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'NNS': 'NN',
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'JJ': 'JJ',
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'JJR': 'JJ',
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'JJS': 'JJ',
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'RB': 'RB',
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'RBR': 'RB',
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'RBS': 'RB',
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'VB': 'VB',
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'VBD': 'VB',
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'VGB': 'VB',
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'VBN': 'VB',
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'VBP': 'VB',
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'VBZ': 'VB'
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}
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for i in range(len(pos_tagged)):
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if pos_tagged[i] in pos_tags:
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pos_tagged[i]=pos_tags[pos_tagged[i]]
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pos_tagged[i] = pos_tags[pos_tagged[i]]
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return pos_tagged
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def _extract_features(self, text):
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feature_set={k:0 for k in self._categories}
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ngrams_words,ngrams_lemmas,pos_tagged = self._extract_ngrams(text)
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matches=0
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pos_tagged=self._clean_pos(pos_tagged)
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feature_set = {k: 0 for k in self._categories}
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ngrams_words, ngrams_lemmas, pos_tagged = self._extract_ngrams(text)
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matches = 0
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pos_tagged = self._clean_pos(pos_tagged)
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tag_wn={'NN':self._wn16.NOUN,'JJ':self._wn16.ADJ,'VB':self._wn16.VERB,'RB':self._wn16.ADV}
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tag_wn = {
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'NN': self._wn16.NOUN,
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'JJ': self._wn16.ADJ,
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'VB': self._wn16.VERB,
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'RB': self._wn16.ADV
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}
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for i in range(len(pos_tagged)):
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if pos_tagged[i] in tag_wn:
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synsets = self._wn16.synsets(ngrams_words[i], tag_wn[pos_tagged[i]])
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synsets = self._wn16.synsets(ngrams_words[i],
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tag_wn[pos_tagged[i]])
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if synsets:
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offset = synsets[0].offset()
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if offset in self._total_synsets[pos_tagged[i]]:
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if self._total_synsets[pos_tagged[i]][offset] is None:
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continue
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else:
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emotion = self._total_synsets[pos_tagged[i]][offset].get_level(5).name
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matches+=1
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emotion = self._total_synsets[pos_tagged[i]][
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offset].get_level(5).name
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matches += 1
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for i in self._categories:
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if emotion in self._categories[i]:
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feature_set[i]+=1
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feature_set[i] += 1
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if matches == 0:
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matches=1
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matches = 1
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for i in feature_set:
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feature_set[i] = (feature_set[i]/matches)*100
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feature_set[i] = (feature_set[i] / matches) * 100
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return feature_set
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@ -166,19 +206,19 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
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text_input = entry.get("text", None)
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text=self._my_preprocessor(text_input)
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text = self._my_preprocessor(text_input)
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feature_text=self._extract_features(text)
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response = Results()
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feature_text = self._extract_features(text)
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emotionSet = EmotionSet(id="Emotions0")
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emotions = emotionSet.onyx__hasEmotion
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for i in feature_text:
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emotions.append(Emotion(onyx__hasEmotionCategory=self._wnaffect_mappings[i],
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onyx__hasEmotionIntensity=feature_text[i]))
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emotions.append(
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Emotion(
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onyx__hasEmotionCategory=self._wnaffect_mappings[i],
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onyx__hasEmotionIntensity=feature_text[i]))
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entry.emotions = [emotionSet]
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yield entry
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yield entry
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@ -22,5 +22,4 @@ onyx:usesEmotionModel: emoml:big6
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requirements:
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- nltk>=3.0.5
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- lxml>=3.4.2
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- pattern
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async: false
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async: false
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@ -1,6 +1,4 @@
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# coding: utf-8
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"""
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Clement Michard (c) 2015
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"""
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@ -85,7 +83,7 @@ class Emotion:
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end_shape = '┐'
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else:
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end_shape = ''
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print '{0}{1}{2}{3}'.format(indent, start_shape, emotion.name, end_shape)
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print ('{0}{1}{2}{3}'.format(indent, start_shape, emotion.name, end_shape))
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for leaf in down:
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next_last = 'down' if down.index(leaf) is len(down) - 1 else ''
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next_indent = '{0}{1}{2}'.format(indent, ' ' if 'down' in last else '│', " " * len(emotion.name))
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@ -22,7 +22,7 @@ class Sentiment140Plugin(SentimentPlugin):
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polarity_value = self.maxPolarityValue*int(res.json()["data"][0]
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["polarity"]) * 0.25
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polarity = "marl:Neutral"
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neutral_value = self.maxPolarityValue / 2.0
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neutral_value = 0
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if polarity_value > neutral_value:
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polarity = "marl:Positive"
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elif polarity_value < neutral_value:
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@ -33,4 +33,4 @@ class Sentiment140Plugin(SentimentPlugin):
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marl__polarityValue=polarity_value)
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entry.sentiments.append(sentiment)
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yield entry
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yield entry
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@ -14,5 +14,5 @@
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},
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"requirements": {},
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"maxPolarityValue": "1",
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"minPolarityValue": "0"
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"minPolarityValue": "-1"
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}
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@ -131,14 +131,16 @@ class SentiTextPlugin(SentimentPlugin):
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if n_pos == 0 and n_neg == 0:
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g_score = 0.5
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polarity = 'marl:Neutral'
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polarity_value = 0
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if g_score > 0.5:
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polarity = 'marl:Positive'
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polarity_value = 1
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elif g_score < 0.5:
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polarity = 'marl:Negative'
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polarity_value = -1
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opinion = Sentiment(id="Opinion0"+'_'+str(i),
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marl__hasPolarity=polarity,
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marL__polarityValue=float("{0:.2f}".format(g_score)))
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marl__polarityValue=polarity_value)
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entry.sentiments.append(opinion)
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@ -18,5 +18,7 @@
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},
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},
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"sentiword_path": "SentiWordNet_3.0.txt",
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"pos_path": "unigram_spanish.pickle"
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"pos_path": "unigram_spanish.pickle",
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"maxPolarityValue": "1",
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"minPolarityValue": "-1"
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}
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