mirror of
https://github.com/gsi-upm/senpy
synced 2025-08-24 02:22:20 +00:00
Merged into monorepo
This commit is contained in:
J. Fernando Sánchez
3
sentiment-basic/.gitmodules
vendored
3
sentiment-basic/.gitmodules
vendored
@@ -1,3 +0,0 @@
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[submodule "data"]
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path = data
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url = ../data/sentiment-basic
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@@ -1,5 +1,6 @@
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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import os
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import logging
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import string
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import nltk
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import pickle
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@@ -13,24 +14,40 @@ from os import path
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from senpy.plugins import SentimentPlugin, SenpyPlugin
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from senpy.models import Results, Entry, Sentiment
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logger = logging.getLogger(__name__)
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class SentiTextPlugin(SentimentPlugin):
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class SentimentBasic(SentimentPlugin):
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'''
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Sentiment classifier using rule-based classification for Spanish. Based on english to spanish translation and SentiWordNet sentiment knowledge. This is a demo plugin that uses only some features from the TASS 2015 classifier. To use the entirely functional classifier you can use the service in: http://senpy.cluster.gsi.dit.upm.es.
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'''
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name = "sentiment-basic"
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author = "github.com/nachtkatze"
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version = "0.1.1"
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extra_params = {
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"language": {
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"aliases": ["language", "l"],
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"required": True,
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"options": ["en","es", "it", "fr", "auto"],
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"default": "auto"
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}
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}
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sentiword_path = "SentiWordNet_3.0.txt"
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pos_path = "unigram_spanish.pickle"
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maxPolarityValue = 1
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minPolarityValue = -1
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nltk_resources = ['punkt','wordnet']
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def _load_swn(self):
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self.swn_path = path.join(path.abspath(path.dirname(__file__)), self.sentiword_path)
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self.swn_path = self.find_file(self.sentiword_path)
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swn = SentiWordNet(self.swn_path)
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return swn
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def _load_pos_tagger(self):
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self.pos_path = path.join(path.abspath(path.dirname(__file__)), self.pos_path)
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self.pos_path = self.find_file(self.pos_path)
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with open(self.pos_path, 'r') as f:
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tagger = pickle.load(f)
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return tagger
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def activate(self, *args, **kwargs):
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nltk.download(['punkt','wordnet'])
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self._swn = self._load_swn()
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self._pos_tagger = self._load_pos_tagger()
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@@ -54,11 +71,6 @@ class SentiTextPlugin(SentimentPlugin):
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tokens[i]['tokens'] = self._pos_tagger.tag(tokens[i]['tokens'])
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return tokens
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# def _stopwords(sentences, lang='english'):
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# for i in sentences:
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# sentences[i]['tokens'] = [t for t in sentences[i]['tokens'] if t not in nltk.corpus.stopwords.words(lang)]
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# return sentences
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def _compare_synsets(self, synsets, tokens, i):
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for synset in synsets:
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for word in tokens[i]['lemmas']:
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@@ -71,7 +83,7 @@ class SentiTextPlugin(SentimentPlugin):
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def analyse_entry(self, entry, params):
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language = params.get("language")
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text = entry.get("text", None)
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text = entry.text
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tokens = self._tokenize(text)
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tokens = self._pos(tokens)
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sufixes = {'es':'spa','en':'eng','it':'ita','fr':'fra'}
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@@ -130,19 +142,41 @@ class SentiTextPlugin(SentimentPlugin):
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except:
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if n_pos == 0 and n_neg == 0:
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g_score = 0.5
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polarity = 'marl:Neutral'
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polarity_value = 0
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if g_score > 0.5:
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if g_score >= 0.5:
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polarity = 'marl:Positive'
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polarity_value = 1
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elif g_score < 0.5:
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polarity = 'marl:Negative'
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polarity_value = -1
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else:
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polarity = 'marl:Neutral'
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polarity_value = 0
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opinion = Sentiment(id="Opinion0"+'_'+str(i),
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marl__hasPolarity=polarity,
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marl__polarityValue=polarity_value)
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opinion.prov(self)
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entry.sentiments.append(opinion)
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yield entry
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test_cases = [
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{
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'input': u'Odio ir al cine',
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'params': {'language': 'es'},
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'polarity': 'marl:Negative'
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},
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{
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'input': u'El cielo está nublado',
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'params': {'language': 'es'},
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'polarity': 'marl:Positive'
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},
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{
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'input': u'Esta tarta está muy buena',
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'params': {'language': 'es'},
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'polarity': 'marl:Negative'
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}
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]
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@@ -1,24 +1,7 @@
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{
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"name": "sentiment-basic",
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"module": "sentiment-basic",
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"description": "Sentiment classifier using rule-based classification for Spanish. Based on english to spanish translation and SentiWordNet sentiment knowledge. This is a demo plugin that uses only some features from the TASS 2015 classifier. To use the entirely functional classifier you can use the service in: http://senpy.cluster.gsi.dit.upm.es.",
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"author": "github.com/nachtkatze",
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"version": "0.1",
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"requirements": [
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"nltk>=3.0.5",
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"scipy>=0.14.0",
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"textblob"
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],
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"extra_params": {
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"language": {
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"aliases": ["language", "l"],
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"required": true,
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"options": ["en","es", "it", "fr", "auto"],
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"default": "auto"
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},
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},
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"sentiword_path": "data/SentiWordNet_3.0.txt",
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"pos_path": "data/unigram_spanish.pickle",
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"maxPolarityValue": "1",
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"minPolarityValue": "-1"
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}
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---
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module: sentiment-basic
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requirements:
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- nltk>=3.0.5
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- scipy>=0.14.0
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- textblob
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@@ -46,7 +46,7 @@ class SentiWordNet(object):
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pos,syn_set_id,pos_score,neg_score,syn_set_score,\
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gloss = fields
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except:
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print "Found data without all details"
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print("Found data without all details")
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pass
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if pos and syn_set_score:
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@@ -67,4 +67,4 @@ class SentiWordNet(object):
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senti_scores.append({"pos":pos_val,"neg":neg_val,\
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"obj": 1.0 - (pos_val - neg_val),'synset':synset})
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return senti_scores
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return senti_scores
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@@ -1,42 +0,0 @@
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import os
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import logging
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logging.basicConfig()
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try:
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import unittest.mock as mock
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except ImportError:
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import mock
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from senpy.extensions import Senpy
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from flask import Flask
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import unittest
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class SentiTextTest(unittest.TestCase):
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def setUp(self):
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self.app = Flask("test_plugin")
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self.dir = os.path.join(os.path.dirname(__file__))
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self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
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self.senpy.init_app(self.app)
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def tearDown(self):
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self.senpy.deactivate_plugin("SentiText", sync=True)
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def test_analyse(self):
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plugin = self.senpy.plugins["SentiText"]
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plugin.activate()
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texts = {'Odio ir al cine' : 'marl:Neutral',
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'El cielo esta nublado' : 'marl:Positive',
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'Esta tarta esta muy buena' : 'marl:Neutral'}
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for text in texts:
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response = plugin.analyse(input=text)
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sentimentSet = response.entries[0].sentiments[0]
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print sentimentSet
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expected = texts[text]
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assert sentimentSet['marl:hasPolarity'] == expected
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plugin.deactivate()
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if __name__ == '__main__':
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unittest.main()
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