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depechemood updated
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Oscar Araque
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159
emotion-depechemood/depechemood_plugin.py
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159
emotion-depechemood/depechemood_plugin.py
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#!/usr/local/bin/python
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# coding: utf-8
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import os
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import re
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import string
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import numpy as np
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import pandas as pd
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from six.moves import urllib
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from nltk.corpus import stopwords
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from senpy import EmotionPlugin, TextBox, models
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class DepecheMood(TextBox, EmotionPlugin):
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'''Plugin that uses the DepecheMood++ emotion lexicon.'''
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author = 'Oscar Araque'
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version = '0.1'
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def __init__(self, *args, **kwargs):
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super(DepecheMood, self).__init__(*args, **kwargs)
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self.LEXICON_URL = "https://github.com/marcoguerini/DepecheMood/raw/master/DepecheMood%2B%2B/DepecheMood_english_token_full.tsv"
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self.EMOTIONS = ['AFRAID', 'AMUSED', 'ANGRY', 'ANNOYED', 'DONT_CARE', 'HAPPY', 'INSPIRED', 'SAD',]
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self._mapping = {
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'AFRAID': 'wna:negative-fear',
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'AMUSED': 'wna:amusement',
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'ANGRY': 'wna:anger',
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'ANNOYED': 'wna:annoyance',
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'DONT_CARE': 'wna:indifference',
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'HAPPY': 'wna:joy',
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'INSPIRED': 'wna:awe',
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'SAD': 'wna:sadness',
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}
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self._noise = self.__noise()
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self._stop_words = stopwords.words('english') + ['']
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self._lex_vocab = None
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self._lex = None
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def __noise(self):
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noise = set(string.punctuation) | set('«»')
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noise = {ord(c): None for c in noise}
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return noise
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def activate(self):
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self._lex = self.download_lex()
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self._lex_vocab = set(list(self._lex.keys()))
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def clean_str(self, string):
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string = re.sub(r"[^A-Za-z0-9().,!?\'\`]", " ", string)
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string = re.sub(r"[0-9]+", " num ", string)
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string = re.sub(r"\'s", " \'s", string)
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string = re.sub(r"\'ve", " \'ve", string)
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string = re.sub(r"n\'t", " n\'t", string)
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string = re.sub(r"\'re", " \'re", string)
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string = re.sub(r"\'d", " \'d", string)
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string = re.sub(r"\'ll", " \'ll", string)
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string = re.sub(r"\.", " . ", string)
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string = re.sub(r",", " , ", string)
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string = re.sub(r"!", " ! ", string)
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string = re.sub(r"\(", " ( ", string)
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string = re.sub(r"\)", " ) ", string)
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string = re.sub(r"\?", " ? ", string)
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string = re.sub(r"\s{2,}", " ", string)
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return string.strip().lower()
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def preprocess(self, text):
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if text is None:
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return None
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tokens = self.clean_str(text).translate(self._noise).split(' ')
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tokens = [tok for tok in tokens if tok not in self._stop_words]
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return tokens
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def estimate_emotion(self, tokens, emotion):
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s = []
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for tok in tokens:
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s.append(self._lex[tok][emotion])
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dividend = np.sum(s) if np.sum(s) > 0 else 0
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divisor = len(s) if len(s) > 0 else 1
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S = np.sum(s) / divisor
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return S
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def estimate_all_emotions(self, tokens):
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S = {}
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intersection = set(tokens) & self._lex_vocab
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for emotion in self.EMOTIONS:
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s = self.estimate_emotion(intersection, emotion)
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emotion_mapped = self._mapping[emotion]
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S[emotion_mapped] = s
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return S
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def download_lex(self, file_path='DepecheMood_english_token_full.tsv', freq_threshold=10):
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try:
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file_path = self.find_file(file_path)
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except IOError:
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filename, _ = urllib.request.urlretrieve(self.LEXICON_URL, file_path)
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lexicon = pd.read_csv(file_path, sep='\t', index_col=0)
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lexicon = lexicon[lexicon['freq'] >= freq_threshold]
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lexicon.drop('freq', axis=1, inplace=True)
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lexicon = lexicon.T.to_dict()
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return lexicon
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def output(self, output, entry, **kwargs):
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s = models.EmotionSet()
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s.prov__wasGeneratedBy = self.id
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entry.emotions.append(s)
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for label, value in output.items():
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e = models.Emotion(onyx__hasEmotionCategory=label,
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onyx__hasEmotionIntensity=value)
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s.onyx__hasEmotion.append(e)
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return entry
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def predict_one(self, input, **kwargs):
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tokens = self.preprocess(input)
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estimation = self.estimate_all_emotions(tokens)
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return estimation
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test_cases = [
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{
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'entry': {
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'nif:isString': 'My cat is very happy',
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},
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'expected': {
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'emotions': [
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{
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'@type': 'emotionSet',
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'onyx:hasEmotion': [
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:negative-fear',
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'onyx:hasEmotionIntensity': 0.05278117640010922, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:amusement',
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'onyx:hasEmotionIntensity': 0.2114806151413433, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:anger',
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'onyx:hasEmotionIntensity': 0.05726119426520887, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:annoyance',
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'onyx:hasEmotionIntensity': 0.12295990731053638, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:indifference',
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'onyx:hasEmotionIntensity': 0.1860159893608025, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:joy',
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'onyx:hasEmotionIntensity': 0.12904050973724163, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:awe',
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'onyx:hasEmotionIntensity': 0.17973650399862967, },
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{'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:sadness',
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'onyx:hasEmotionIntensity': 0.060724103786128455, },
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]
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}
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]
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}
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}
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]
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if __name__ == '__main__':
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from senpy.utils import easy, easy_load, easy_test
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# sp, app = easy_load()
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# for plug in sp.analysis_plugins:
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# plug.test()
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easy()
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