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https://github.com/gsi-upm/senpy
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Update to senpy 0.20
This commit is contained in:
J. Fernando Sánchez
@@ -19,11 +19,11 @@ from nltk.corpus import stopwords
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from pattern.en import parse as parse_en
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from pattern.es import parse as parse_es
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from senpy.plugins import SentimentPlugin, SenpyPlugin
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from senpy.plugins import EmotionPlugin, SenpyPlugin
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from senpy.models import Results, EmotionSet, Entry, Emotion
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class ANEW(SentimentPlugin):
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class ANEW(EmotionPlugin):
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description = "This plugin consists on an emotion classifier using ANEW lexicon dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. Each emotion has a centroid, calculated according to this article: http://www.aclweb.org/anthology/W10-0208. The plugin is going to look for the words in the sentence that appear in the ANEW dictionary and calculate the average VAD score for the sentence. Once this score is calculated, it is going to seek the emotion that is closest to this value."
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author = "@icorcuera"
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version = "0.5.1"
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@@ -31,6 +31,7 @@ class ANEW(SentimentPlugin):
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extra_params = {
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"language": {
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"description": "language of the input",
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"aliases": ["language", "l"],
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"required": True,
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"options": ["es","en"],
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@@ -75,7 +76,7 @@ class ANEW(SentimentPlugin):
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"neutral": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#neutral-emotion",
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"sadness": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#sadness"
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}
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onyx__usesEmotionModel = "emoml:big6"
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onyx__usesEmotionModel = "emoml:pad"
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nltk_resources = ['stopwords']
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def activate(self, *args, **kwargs):
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@@ -119,14 +120,13 @@ class ANEW(SentimentPlugin):
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return s
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def _extract_ngrams(self, text, lang):
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unigrams_lemmas = []
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unigrams_words = []
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pos_tagged = []
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if lang == 'es':
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sentences = parse_es(text,lemmata=True).split()
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sentences = list(parse_es(text, lemmata=True).split())
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else:
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sentences = parse_en(text,lemmata=True).split()
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sentences = list(parse_en(text, lemmata=True).split())
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for sentence in sentences:
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for token in sentence:
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@@ -140,19 +140,6 @@ class ANEW(SentimentPlugin):
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def _find_ngrams(self, input_list, n):
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return zip(*[input_list[i:] for i in range(n)])
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def _emotion_calculate(self, VAD):
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emotion=''
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value=10000000000000000000000.0
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for state in self.centroids:
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valence=VAD[0]-self.centroids[state]['V']
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arousal=VAD[1]-self.centroids[state]['A']
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dominance=VAD[2]-self.centroids[state]['D']
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new_value=math.sqrt((valence*valence)+(arousal*arousal)+(dominance*dominance))
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if new_value < value:
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value=new_value
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emotion=state
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return emotion
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def _extract_features(self, tweet,dictionary,lang):
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feature_set={}
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ngrams_lemmas,ngrams_words,pos_tagged = self._extract_ngrams(tweet,lang)
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@@ -176,14 +163,13 @@ class ANEW(SentimentPlugin):
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emotion='neutral'
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else:
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totalVAD=[totalVAD[0]/matches,totalVAD[1]/matches,totalVAD[2]/matches]
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emotion=self._emotion_calculate(totalVAD)
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feature_set['emotion']=emotion
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feature_set['V']=totalVAD[0]
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feature_set['A']=totalVAD[1]
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feature_set['D']=totalVAD[2]
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feature_set['V'] = totalVAD[0]
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feature_set['A'] = totalVAD[1]
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feature_set['D'] = totalVAD[2]
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return feature_set
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def analyse_entry(self, entry, params):
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def analyse_entry(self, entry, activity):
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params = activity.params
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text_input = entry.text
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@@ -196,13 +182,12 @@ class ANEW(SentimentPlugin):
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emotions.id = "Emotions0"
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emotion1 = Emotion(id="Emotion0")
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emotion1["onyx:hasEmotionCategory"] = self.emotions_ontology[feature_set['emotion']]
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emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence"] = feature_set['V']
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emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal"] = feature_set['A']
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emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance"] = feature_set['D']
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emotion1.prov(self)
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emotions.prov(self)
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emotion1.prov(activity)
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emotions.prov(activity)
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emotions.onyx__hasEmotion.append(emotion1)
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entry.emotions = [emotions, ]
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@@ -212,47 +197,64 @@ class ANEW(SentimentPlugin):
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ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
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test_cases = [
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{
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'name': 'anger with VAD=(2.12, 6.95, 5.05)',
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'input': 'I hate you',
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'expected': {
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'emotions': [{
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'anger',
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 6.95,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 5.05,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 2.12,
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}]
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}]
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}
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}, {
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'input': 'i am sad',
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'expected': {
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'emotions': [{
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'sadness',
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 4.13,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 3.45,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 1.61,
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}]
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}]
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}
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}, {
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'name': 'joy',
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'input': 'i am happy with my marks',
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'expected': {
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'emotions': [{
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'joy',
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 6.49,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 6.63,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 8.21,
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}]
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}]
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}
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}, {
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'name': 'negative-feat',
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'input': 'This movie is scary',
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'expected': {
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'emotions': [{
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'negative-fear',
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 5.8100000000000005,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 4.33,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 5.050000000000001,
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}]
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}]
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}
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}, {
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'name': 'negative-fear',
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'input': 'this cake is disgusting' ,
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'expected': {
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'emotions': [{
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'onyx:hasEmotionSet': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'negative-fear',
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal": 5.09,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance": 4.4,
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"http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence": 5.109999999999999,
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}]
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}]
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}
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@@ -8,5 +8,4 @@ requirements:
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- scikit-learn
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- textblob
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- pattern
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- lxml
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onyx:usesEmotionModel: "emoml:big6"
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- lxml
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