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Add depeche mood

This commit is contained in:
J. Fernando Sánchez 2018-12-14 18:50:35 +01:00
parent 4507449266
commit 675a905ab4

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#!/usr/local/bin/python
# coding: utf-8
import os
import re
import string
import numpy as np
import pandas as pd
from six.moves import urllib
from nltk.corpus import stopwords
from senpy import EmotionPlugin, TextBox, models
class DepecheMood(TextBox, EmotionPlugin):
'''Plugin that uses the DepecheMood++ emotion lexicon.'''
author = 'Oscar Araque'
version = '0.1'
def __init__(self, *args, **kwargs):
super(DepecheMood, self).__init__(*args, **kwargs)
self.LEXICON_URL = "https://github.com/marcoguerini/DepecheMood/raw/master/DepecheMood%2B%2B/DepecheMood_english_token_full.tsv"
self.EMOTIONS = ['AFRAID', 'AMUSED', 'ANGRY', 'ANNOYED', 'DONT_CARE', 'HAPPY', 'INSPIRED', 'SAD',]
self.noise = self._noise()
self.stop_words = stopwords.words('english') + ['']
def _noise(self):
noise = set(string.punctuation) | set('«»')
noise = {ord(c): None for c in noise}
return noise
def activate(self):
self._lex = self.download_lex()
self._lex_vocab = set(list(self._lex.keys()))
def clean_str(self, string):
string = re.sub(r"[^A-Za-z0-9().,!?\'\`]", " ", string)
string = re.sub(r"[0-9]+", " num ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r"\.", " . ", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " ( ", string)
string = re.sub(r"\)", " ) ", string)
string = re.sub(r"\?", " ? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower()
def preprocess(self, text):
if text is None:
return None
tokens = self.clean_str(text).translate(self.noise).split(' ')
tokens = [tok for tok in tokens if tok not in self.stop_words]
return tokens
def estimate_emotion(self, tokens, emotion):
s = []
for tok in tokens:
s.append(self._lex[tok][emotion])
dividend = np.sum(s) if np.sum(s) > 0 else 0
divisor = len(s) if len(s) > 0 else 1
S = np.sum(s) / divisor
return S
def estimate_all_emotions(self, tokens):
S = {emotion: None for emotion in self.EMOTIONS}
intersection = set(tokens) & self._lex_vocab
for emotion in self.EMOTIONS:
s = self.estimate_emotion(intersection, emotion)
S[emotion] = s
return S
def download_lex(self, file_path='./DepecheMood_english_token_full.tsv', freq_threshold=10):
if not os.path.exists(file_path):
filename, _ = urllib.request.urlretrieve(self.LEXICON_URL, file_path)
lexicon = pd.read_csv(file_path, sep='\t', index_col=0)
lexicon = lexicon[lexicon['freq'] >= 10]
lexicon.drop('freq', axis=1, inplace=True)
lexicon = lexicon.T.to_dict()
return lexicon
def output(self, output, entry, **kwargs):
s = models.EmotionSet()
entry.emotions.append(s)
for label, value in output.items():
e = models.Emotion(onyx__hasEmotionCategory=label,
onyx__hasEmotionIntensity=value)
s.onyx__hasEmotion.append(e)
return entry
def predict_one(self, input, **kwargs):
tokens = self.preprocess(input)
estimation = self.estimate_all_emotions(tokens)
return estimation
test_cases = [
{
'text': ''
}
]
if __name__ == '__main__':
from senpy.utils import easy
easy()