mirror of
https://github.com/gsi-upm/senpy
synced 2025-08-24 02:22:20 +00:00
Update to senpy 0.20
This commit is contained in:
J. Fernando Sánchez
@@ -1,49 +1,56 @@
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#!/usr/local/bin/python
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# coding: utf-8
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from future import standard_library
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standard_library.install_aliases()
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import os
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import re
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import sys
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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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from senpy import EmotionBox, models
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def ignore(dchars):
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deletechars = "".join(dchars)
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if sys.version_info[0] >= 3:
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tbl = str.maketrans("", "", deletechars)
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ignore = lambda s: s.translate(tbl)
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else:
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def ignore(s):
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return string.translate(s, None, deletechars)
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tbl = str.maketrans("", "", deletechars)
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ignore = lambda s: s.translate(tbl)
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return ignore
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class DepecheMood(TextBox, EmotionPlugin):
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'''Plugin that uses the DepecheMood++ emotion lexicon.'''
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class DepecheMood(EmotionBox):
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'''
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Plugin that uses the DepecheMood emotion lexicon.
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DepecheMood is an emotion lexicon automatically generated from news articles where users expressed their associated emotions. It contains two languages (English and Italian), as well as three types of word representations (token, lemma and lemma#PoS). For English, the lexicon contains 165k tokens, while the Italian version contains 116k. Unsupervised techniques can be applied to generate simple but effective baselines. To learn more, please visit https://github.com/marcoguerini/DepecheMood and http://www.depechemood.eu/
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'''
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author = 'Oscar Araque'
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name = 'emotion-depechemood'
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version = '0.1'
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requirements = ['pandas']
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nltk_resources = ["stopwords"]
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onyx__usesEmotionModel = 'wna:WNAModel'
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EMOTIONS = ['wna:negative-fear',
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'wna:amusement',
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'wna:anger',
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'wna:annoyance',
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'wna:indifference',
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'wna:joy',
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'wna:awe',
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'wna:sadness']
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DM_EMOTIONS = ['AFRAID', 'AMUSED', 'ANGRY', 'ANNOYED', 'DONT_CARE', 'HAPPY', 'INSPIRED', 'SAD',]
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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._denoise = ignore(set(string.punctuation)|set('«»'))
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self._stop_words = []
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self._lex_vocab = None
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@@ -89,19 +96,21 @@ class DepecheMood(TextBox, EmotionPlugin):
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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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S = []
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intersection = set(tokens) & self._lex_vocab
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for emotion in self.EMOTIONS:
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for emotion in self.DM_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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S.append(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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import pandas as pd
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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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file_path = self.path(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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@@ -110,18 +119,8 @@ class DepecheMood(TextBox, EmotionPlugin):
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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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def predict_one(self, features, **kwargs):
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tokens = self.preprocess(features[0])
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estimation = self.estimate_all_emotions(tokens)
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return estimation
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@@ -131,26 +130,41 @@ class DepecheMood(TextBox, EmotionPlugin):
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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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'onyx:hasEmotionSet': [
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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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'onyx:hasEmotionCategory': 'wna:negative-fear',
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'onyx:hasEmotionIntensity': 0.05278117640010922
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},
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{
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'onyx:hasEmotionCategory': 'wna:amusement',
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'onyx:hasEmotionIntensity': 0.2114806151413433,
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},
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{
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'onyx:hasEmotionCategory': 'wna:anger',
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'onyx:hasEmotionIntensity': 0.05726119426520887
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},
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{
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'onyx:hasEmotionCategory': 'wna:annoyance',
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'onyx:hasEmotionIntensity': 0.12295990731053638,
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},
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{
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'onyx:hasEmotionCategory': 'wna:indifference',
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'onyx:hasEmotionIntensity': 0.1860159893608025,
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},
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{
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'onyx:hasEmotionCategory': 'wna:joy',
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'onyx:hasEmotionIntensity': 0.12904050973724163,
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},
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{
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'onyx:hasEmotionCategory': 'wna:awe',
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'onyx:hasEmotionIntensity': 0.17973650399862967,
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},
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{
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'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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@@ -164,4 +178,4 @@ if __name__ == '__main__':
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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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easy_test(debug=False)
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