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Code Issues Releases Wiki Activity

Update to senpy 0.20

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This commit is contained in:
J. Fernando Sánchez
2019-04-04 12:56:46 +02:00
parent fa993c6e2a
commit 4f286057c9
13 changed files with 239 additions and 247 deletions
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56
sentiment-vader/vader_plugin.py
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@@ -1,12 +1,12 @@
# -*- coding: utf-8 -*-
from vaderSentiment import sentiment
from senpy.plugins import SentimentPlugin, SenpyPlugin
from senpy.plugins import SentimentBox, SenpyPlugin
from senpy.models import Results, Sentiment, Entry
import logging
class VaderSentimentPlugin(SentimentPlugin):
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.
'''
@@ -16,6 +16,7 @@ class VaderSentimentPlugin(SentimentPlugin):
version = "0.1.1"
extra_params = {
"language": {
"description": "language of the input",
"@id": "lang_rand",
"aliases": ["language", "l"],
"default": "auto",
@@ -23,55 +24,32 @@ class VaderSentimentPlugin(SentimentPlugin):
},
"aggregate": {
"description": "Show only the strongest sentiment (aggregate) or all sentiments",
"aliases": ["aggregate","agg"],
"options": ["true", "false"],
"options": [True, False],
"default": False
}
}
requirements = {}
def analyse_entry(self, entry, params):
_VADER_KEYS = ['pos', 'neu', 'neg']
binary = False
self.log.debug("Analysing with params {}".format(params))
text_input = entry.text
aggregate = params['aggregate']
def predict_one(self, features, activity):
text_input = ' '.join(features)
scores = sentiment(text_input)
score = sentiment(text_input)
sentiments = []
for k in self._VADER_KEYS:
sentiments.append(scores[k])
opinion0 = Sentiment(id= "Opinion_positive",
marl__hasPolarity= "marl:Positive",
marl__algorithmConfidence= score['pos']
)
opinion0.prov(self)
opinion1 = Sentiment(id= "Opinion_negative",
marl__hasPolarity= "marl:Negative",
marl__algorithmConfidence= score['neg']
)
opinion1.prov(self)
opinion2 = Sentiment(id= "Opinion_neutral",
marl__hasPolarity = "marl:Neutral",
marl__algorithmConfidence = score['neu']
)
opinion2.prov(self)
if activity.param('aggregate'):
m = max(sentiments)
sentiments = [k if k==m else None for k in sentiments]
if aggregate == 'true':
res = None
confident = max(score['neg'],score['neu'],score['pos'])
if opinion0.marl__algorithmConfidence == confident:
res = opinion0
elif opinion1.marl__algorithmConfidence == confident:
res = opinion1
elif opinion2.marl__algorithmConfidence == confident:
res = opinion2
entry.sentiments.append(res)
else:
entry.sentiments.append(opinion0)
entry.sentiments.append(opinion1)
entry.sentiments.append(opinion2)
yield entry
return sentiments
test_cases = []