From 94394af20b4526115749c1ffcf63a16e132951a5 Mon Sep 17 00:00:00 2001 From: Oscar Araque Date: Wed, 9 Jan 2019 17:19:22 +0100 Subject: [PATCH] depechemood updated --- emotion-depechemood/depechemood_plugin.py | 159 ++++++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 emotion-depechemood/depechemood_plugin.py diff --git a/emotion-depechemood/depechemood_plugin.py b/emotion-depechemood/depechemood_plugin.py new file mode 100644 index 0000000..25389be --- /dev/null +++ b/emotion-depechemood/depechemood_plugin.py @@ -0,0 +1,159 @@ +#!/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._mapping = { + 'AFRAID': 'wna:negative-fear', + 'AMUSED': 'wna:amusement', + 'ANGRY': 'wna:anger', + 'ANNOYED': 'wna:annoyance', + 'DONT_CARE': 'wna:indifference', + 'HAPPY': 'wna:joy', + 'INSPIRED': 'wna:awe', + 'SAD': 'wna:sadness', + } + self._noise = self.__noise() + self._stop_words = stopwords.words('english') + [''] + self._lex_vocab = None + self._lex = None + + 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 = {} + intersection = set(tokens) & self._lex_vocab + for emotion in self.EMOTIONS: + s = self.estimate_emotion(intersection, emotion) + emotion_mapped = self._mapping[emotion] + S[emotion_mapped] = s + return S + + def download_lex(self, file_path='DepecheMood_english_token_full.tsv', freq_threshold=10): + + try: + file_path = self.find_file(file_path) + except IOError: + filename, _ = urllib.request.urlretrieve(self.LEXICON_URL, file_path) + + lexicon = pd.read_csv(file_path, sep='\t', index_col=0) + lexicon = lexicon[lexicon['freq'] >= freq_threshold] + lexicon.drop('freq', axis=1, inplace=True) + lexicon = lexicon.T.to_dict() + return lexicon + + def output(self, output, entry, **kwargs): + s = models.EmotionSet() + s.prov__wasGeneratedBy = self.id + 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 = [ + { + 'entry': { + 'nif:isString': 'My cat is very happy', + }, + 'expected': { + 'emotions': [ + { + '@type': 'emotionSet', + 'onyx:hasEmotion': [ + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:negative-fear', + 'onyx:hasEmotionIntensity': 0.05278117640010922, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:amusement', + 'onyx:hasEmotionIntensity': 0.2114806151413433, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:anger', + 'onyx:hasEmotionIntensity': 0.05726119426520887, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:annoyance', + 'onyx:hasEmotionIntensity': 0.12295990731053638, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:indifference', + 'onyx:hasEmotionIntensity': 0.1860159893608025, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:joy', + 'onyx:hasEmotionIntensity': 0.12904050973724163, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:awe', + 'onyx:hasEmotionIntensity': 0.17973650399862967, }, + {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:sadness', + 'onyx:hasEmotionIntensity': 0.060724103786128455, }, + ] + } + ] + } + } + ] + + +if __name__ == '__main__': + from senpy.utils import easy, easy_load, easy_test + # sp, app = easy_load() + # for plug in sp.analysis_plugins: + # plug.test() + easy()