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git-subtree-dir: sentiment-basic
git-subtree-split: beb8e311619059a0c660411edef1cf95b3826c0a
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J. Fernando Sánchez 2018-06-12 10:01:45 +02:00
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[submodule "data"]
path = data
url = ../data/sentiment-basic

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# Sentiment basic plugin
This plugin is based on the classifier developed for the TASS 2015 competition. It has been developed for Spanish and English. 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
There is more information avaliable in:
- Aspect based Sentiment Analysis of Spanish Tweets, Oscar Araque and Ignacio Corcuera-Platas and Constantino Román-Gómez and Carlos A. Iglesias and J. Fernando Sánchez-Rada. http://gsi.dit.upm.es/es/investigacion/publicaciones?view=publication&task=show&id=376
## Usage
Params accepted:
- Language: Spanish (es).
- Input: text to analyse.
Example request:
```
http://senpy.cluster.gsi.dit.upm.es/api/?algo=sentiment-basic&language=es&input=I%20love%20Madrid
```
Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section.
This plugin only supports **python2**
![alt GSI Logo][logoGSI]
[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"

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Subproject commit 7f99680db607fd06dc46009a7dae13ca4fc4e6ce

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import os
import logging
import string
import nltk
import pickle
from sentiwn import SentiWordNet
from nltk.corpus import wordnet as wn
from textblob import TextBlob
from scipy.interpolate import interp1d
from os import path
from senpy.plugins import SentimentPlugin, SenpyPlugin
from senpy.models import Results, Entry, Sentiment
logger = logging.getLogger(__name__)
class SentiTextPlugin(SentimentPlugin):
def _load_swn(self):
self.swn_path = path.join(path.abspath(path.dirname(__file__)), self.sentiword_path)
swn = SentiWordNet(self.swn_path)
return swn
def _load_pos_tagger(self):
self.pos_path = path.join(path.abspath(path.dirname(__file__)), self.pos_path)
with open(self.pos_path, 'r') as f:
tagger = pickle.load(f)
return tagger
def activate(self, *args, **kwargs):
nltk.download(['punkt','wordnet'])
self._swn = self._load_swn()
self._pos_tagger = self._load_pos_tagger()
def _remove_punctuation(self, tokens):
return [t for t in tokens if t not in string.punctuation]
def _tokenize(self, text):
data = {}
sentences = nltk.sent_tokenize(text)
for i, sentence in enumerate(sentences):
sentence_ = {}
words = nltk.word_tokenize(sentence)
sentence_['sentence'] = sentence
tokens_ = [w.lower() for w in words]
sentence_['tokens'] = self._remove_punctuation(tokens_)
data[i] = sentence_
return data
def _pos(self, tokens):
for i in tokens:
tokens[i]['tokens'] = self._pos_tagger.tag(tokens[i]['tokens'])
return tokens
# def _stopwords(sentences, lang='english'):
# for i in sentences:
# sentences[i]['tokens'] = [t for t in sentences[i]['tokens'] if t not in nltk.corpus.stopwords.words(lang)]
# return sentences
def _compare_synsets(self, synsets, tokens, i):
for synset in synsets:
for word in tokens[i]['lemmas']:
for lemma in tokens[i]['lemmas'][word]:
synset_ = lemma.synset()
if synset == synset_:
return synset
return None
def analyse_entry(self, entry, params):
language = params.get("language")
text = entry.get("text", None)
tokens = self._tokenize(text)
tokens = self._pos(tokens)
sufixes = {'es':'spa','en':'eng','it':'ita','fr':'fra'}
for i in tokens:
tokens[i]['lemmas'] = {}
for w in tokens[i]['tokens']:
lemmas = wn.lemmas(w[0], lang=sufixes[language])
if len(lemmas) == 0:
continue
tokens[i]['lemmas'][w[0]] = lemmas
if language == "en":
trans = TextBlob(unicode(text))
else:
trans = TextBlob(unicode(text)).translate(from_lang=language,to='en')
useful_synsets = {}
for s_i, t_s in enumerate(trans.sentences):
useful_synsets[s_i] = {}
for w_i, t_w in enumerate(trans.sentences[s_i].words):
synsets = wn.synsets(trans.sentences[s_i].words[w_i])
if len(synsets) == 0:
continue
eq_synset = self._compare_synsets(synsets, tokens, s_i)
useful_synsets[s_i][t_w] = eq_synset
scores = {}
for i in tokens:
scores[i] = {}
if useful_synsets != None:
for word in useful_synsets[i]:
if useful_synsets[i][word] is None:
continue
temp_scores = self._swn.get_score(useful_synsets[i][word].name().split('.')[0].replace(' ',' '))
for score in temp_scores:
if score['synset'] == useful_synsets[i][word]:
t_score = score['pos'] - score['neg']
f_score = 'neu'
if t_score > 0:
f_score = 'pos'
elif t_score < 0:
f_score = 'neg'
score['score'] = f_score
scores[i][word] = score
break
p = params.get("prefix", None)
for i in scores:
n_pos = 0.0
n_neg = 0.0
for w in scores[i]:
if scores[i][w]['score'] == 'pos':
n_pos += 1.0
elif scores[i][w]['score'] == 'neg':
n_neg += 1.0
inter = interp1d([-1.0, 1.0], [0.0, 1.0])
try:
g_score = (n_pos - n_neg) / (n_pos + n_neg)
g_score = float(inter(g_score))
except:
if n_pos == 0 and n_neg == 0:
g_score = 0.5
polarity = 'marl:Neutral'
polarity_value = 0
if g_score > 0.5:
polarity = 'marl:Positive'
polarity_value = 1
elif g_score < 0.5:
polarity = 'marl:Negative'
polarity_value = -1
opinion = Sentiment(id="Opinion0"+'_'+str(i),
marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
entry.sentiments.append(opinion)
yield entry

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{
"name": "sentiment-basic",
"module": "sentiment-basic",
"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.",
"author": "github.com/nachtkatze",
"version": "0.1",
"requirements": [
"nltk>=3.0.5",
"scipy>=0.14.0",
"textblob"
],
"extra_params": {
"language": {
"aliases": ["language", "l"],
"required": true,
"options": ["en","es", "it", "fr", "auto"],
"default": "auto"
},
},
"sentiword_path": "data/SentiWordNet_3.0.txt",
"pos_path": "data/unigram_spanish.pickle",
"maxPolarityValue": "1",
"minPolarityValue": "-1"
}

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#!/usr/bin/env python
"""
Author : Jaganadh Gopinadhan <jaganadhg@gmail.com>
Copywright (C) : Jaganadh Gopinadhan
Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import sys,os
import re
from nltk.corpus import wordnet
class SentiWordNet(object):
"""
Interface to SentiWordNet
"""
def __init__(self,swn_file):
"""
"""
self.swn_file = swn_file
self.pos_synset = self.__parse_swn_file()
def __parse_swn_file(self):
"""
Parse the SentiWordNet file and populate the POS and SynsetID hash
"""
pos_synset_hash = {}
swn_data = open(self.swn_file,'r').readlines()
head_less_swn_data = filter((lambda line: not re.search(r"^\s*#",\
line)), swn_data)
for data in head_less_swn_data:
fields = data.strip().split("\t")
try:
pos,syn_set_id,pos_score,neg_score,syn_set_score,\
gloss = fields
except:
print "Found data without all details"
pass
if pos and syn_set_score:
pos_synset_hash[(pos,int(syn_set_id))] = (float(pos_score),\
float(neg_score))
return pos_synset_hash
def get_score(self,word,pos=None):
"""
Get score for a given word/word pos combination
"""
senti_scores = []
synsets = wordnet.synsets(word,pos)
for synset in synsets:
if self.pos_synset.has_key((synset.pos(), synset.offset())):
pos_val, neg_val = self.pos_synset[(synset.pos(), synset.offset())]
senti_scores.append({"pos":pos_val,"neg":neg_val,\
"obj": 1.0 - (pos_val - neg_val),'synset':synset})
return senti_scores

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import os
import logging
logging.basicConfig()
try:
import unittest.mock as mock
except ImportError:
import mock
from senpy.extensions import Senpy
from flask import Flask
import unittest
class SentiTextTest(unittest.TestCase):
def setUp(self):
self.app = Flask("test_plugin")
self.dir = os.path.join(os.path.dirname(__file__))
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
self.senpy.init_app(self.app)
def tearDown(self):
self.senpy.deactivate_plugin("SentiText", sync=True)
def test_analyse(self):
plugin = self.senpy.plugins["SentiText"]
plugin.activate()
texts = {'Odio ir al cine' : 'marl:Neutral',
'El cielo esta nublado' : 'marl:Positive',
'Esta tarta esta muy buena' : 'marl:Neutral'}
for text in texts:
response = plugin.analyse(input=text)
sentimentSet = response.entries[0].sentiments[0]
print sentimentSet
expected = texts[text]
assert sentimentSet['marl:hasPolarity'] == expected
plugin.deactivate()
if __name__ == '__main__':
unittest.main()