mirror of
https://github.com/gsi-upm/senpy
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75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
# -*- coding: utf-8 -*-
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from vaderSentiment import sentiment
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from senpy.plugins import SentimentBox, SenpyPlugin
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from senpy.models import Results, Sentiment, Entry
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import logging
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class VaderSentimentPlugin(SentimentBox):
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'''
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Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.
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'''
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name = "sentiment-vader"
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module = "sentiment-vader"
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author = "@icorcuera"
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version = "0.1.1"
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extra_params = {
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"language": {
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"description": "language of the input",
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"@id": "lang_rand",
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"aliases": ["language", "l"],
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"default": "auto",
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"options": ["es", "en", "auto"]
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},
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"aggregate": {
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"description": "Show only the strongest sentiment (aggregate) or all sentiments",
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"aliases": ["aggregate","agg"],
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"options": [True, False],
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"default": False
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}
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}
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requirements = {}
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_VADER_KEYS = ['pos', 'neu', 'neg']
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binary = False
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def predict_one(self, features, activity):
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text_input = ' '.join(features)
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scores = sentiment(text_input)
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sentiments = []
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for k in self._VADER_KEYS:
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sentiments.append(scores[k])
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if activity.param('aggregate'):
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m = max(sentiments)
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sentiments = [k if k==m else None for k in sentiments]
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return sentiments
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test_cases = []
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test_cases = [
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{
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'input': 'I am tired :(',
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'polarity': 'marl:Negative'
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},
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{
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'input': 'I love pizza :(',
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'polarity': 'marl:Positive'
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},
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{
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'input': 'I enjoy going to the cinema :)',
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'polarity': 'marl:Negative'
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},
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{
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'input': 'This cake is disgusting',
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'polarity': 'marl:Negative'
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},
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]
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