In [1]:

    

import pickle
import re
import os
from vectorizer import vect
clf = pickle.load(open(
        os.path.join('pkl_objects',
                     'classifier.pkl'), 'rb'))


    

In [3]:

    

import numpy as np
label = {0: 'negative', 1: 'positive'}
example = ['I love this movie']
X = vect.transform(example)
print('Prediction: %s\nProbability: %.2f%%' %\
      (label[clf.predict(X)[0]],
       np.max(clf.predict_proba(X))*100))


    

    




Prediction: positive
Probability: 82.81%

In [8]:

    

import sqlite3
import os
conn = sqlite3.connect('reviews.sqlite')
c = conn.cursor()
c.execute('CREATE TABLE review_db'\
          ' (review TEXT, sentiment INTEGER, date TEXT)')
example1 = 'I love this movie'
c.execute("INSERT INTO review_db"\
          " (review, sentiment, date) VALUES"\
          " (?, ?, DATETIME('now'))", (example1, 1))
example2 = 'I disliked this movie'
c.execute("INSERT INTO review_db"\
          " (review, sentiment, date) VALUES"\
          " (?, ?, DATETIME('now'))", (example2, 0))
conn.commit()
conn.close()


    

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