mirror of
https://github.com/gsi-upm/senpy
synced 2024-11-14 12:42:27 +00:00
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
#!/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.noise = self._noise()
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self.stop_words = stopwords.words('english') + ['']
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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 = {emotion: None for emotion in self.EMOTIONS}
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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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S[emotion] = 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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if not os.path.exists(file_path):
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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'] >= 10]
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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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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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'text': ''
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}
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]
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if __name__ == '__main__':
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from senpy.utils import easy
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easy()
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