mirror of https://github.com/gsi-upm/senpy
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
# -*- coding: utf-8 -*-
|
|
|
|
from vaderSentiment import sentiment
|
|
from senpy.plugins import SentimentBox, SenpyPlugin
|
|
from senpy.models import Results, Sentiment, Entry
|
|
import logging
|
|
|
|
|
|
class VaderSentimentPlugin(SentimentBox):
|
|
'''
|
|
Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.
|
|
'''
|
|
name = "sentiment-vader"
|
|
module = "sentiment-vader"
|
|
author = "@icorcuera"
|
|
version = "0.1.1"
|
|
extra_params = {
|
|
"language": {
|
|
"description": "language of the input",
|
|
"@id": "lang_rand",
|
|
"aliases": ["language", "l"],
|
|
"default": "auto",
|
|
"options": ["es", "en", "auto"]
|
|
},
|
|
|
|
"aggregate": {
|
|
"description": "Show only the strongest sentiment (aggregate) or all sentiments",
|
|
"aliases": ["aggregate","agg"],
|
|
"options": [True, False],
|
|
"default": False
|
|
}
|
|
|
|
}
|
|
requirements = {}
|
|
|
|
_VADER_KEYS = ['pos', 'neu', 'neg']
|
|
binary = False
|
|
|
|
|
|
def predict_one(self, features, activity):
|
|
text_input = ' '.join(features)
|
|
scores = sentiment(text_input)
|
|
|
|
sentiments = []
|
|
for k in self._VADER_KEYS:
|
|
sentiments.append(scores[k])
|
|
|
|
if activity.param('aggregate'):
|
|
m = max(sentiments)
|
|
sentiments = [k if k==m else None for k in sentiments]
|
|
|
|
return sentiments
|
|
|
|
test_cases = []
|
|
|
|
test_cases = [
|
|
{
|
|
'input': 'I am tired :(',
|
|
'polarity': 'marl:Negative'
|
|
},
|
|
{
|
|
'input': 'I love pizza :(',
|
|
'polarity': 'marl:Positive'
|
|
},
|
|
{
|
|
'input': 'I enjoy going to the cinema :)',
|
|
'polarity': 'marl:Negative'
|
|
},
|
|
{
|
|
'input': 'This cake is disgusting',
|
|
'polarity': 'marl:Negative'
|
|
},
|
|
|
|
]
|