sentiment_analysis.py

import nltk
import random
import pickle

from nltk.tokenize import word_tokenize
from nltk.classify.scikitlearn import SklearnClassifier
from nltk.classify import ClassifierI

from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC

from statistics import mode

class VoteClassifier(ClassifierI):
    def __init__(self, *classifiers):
        self._classifiers = classifiers
    
    def classify(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)
        return mode(votes)
    
    def confidence(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)
        choice_votes = votes.count(mode(votes))
        conf = choice_votes / len(votes)
        return conf


documents_f = open("pickled_files/documents.pickle", "rb")
documents = pickle.load(documents_f)
documents_f.close()

word_features5k_f = open("pickled_files/word_features5k.pickle", "rb")
word_features = pickle.load(word_features5k_f)
word_features5k_f.close()


def find_features(document):
    words = word_tokenize(document)
    features = {}
    for w in word_features:
        features[w] = (w in words)

    return features


featuresets_f = open("pickled_files/featuresets.pickle", "rb")
featuresets = pickle.load(featuresets_f)
featuresets_f.close()

random.shuffle(featuresets)
# print(len(featuresets))

testing_set = featuresets[10000:]
training_set = featuresets[:10000]

open_file = open("pickled_files/originalnaivebayes5k.pickle", "rb")
classifier = pickle.load(open_file)
open_file.close()

open_file = open("pickled_files/MNB_classifier5k.pickle", "rb")
MNB_classifier = pickle.load(open_file)
open_file.close()

open_file = open("pickled_files/BernoulliNB_classifier5k.pickle", "rb")
BernoulliNB_classifier = pickle.load(open_file)
open_file.close()

open_file = open("pickled_files/LogisticRegression_classifier5k.pickle", "rb")
LogisticRegression_classifier = pickle.load(open_file)
open_file.close()

open_file = open("pickled_files/LinearSVC_classifier5k.pickle", "rb")
LinearSVC_classifier = pickle.load(open_file)
open_file.close()

open_file = open("pickled_files/SGDC_classifier5k.pickle", "rb")
SGDC_classifier = pickle.load(open_file)
open_file.close()


voted_classifier = VoteClassifier(
  classifier,
  LinearSVC_classifier,
  MNB_classifier,
  BernoulliNB_classifier,
  LogisticRegression_classifier)

def sentiment(text):
    feats = find_features(text)
    return voted_classifier.classify(feats),voted_classifier.confidence(feats)