Intro

A little tutorial to show how to build and train a classifier to distinguish positive from negative reviews:

as an example dataset we download Movie Reviews from Kaggle.

This dataset contains 1000 positive and 1000 negative processed reviews.

link: https://www.kaggle.com/nltkdata/movie-review

Scikit-learn is a free software machine learning library for the Python programming language.

It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

https://scikit-learn.org/stable/

Classifier

use BernoulliNB Naive Bayes classifier for multivariate Bernoulli models.

Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.

https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html

CountVectorizer

Convert a collection of text documents to a matrix of token counts

This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix.

If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data.

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

Packages

import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import BernoulliNB from sklearn.metrics import accuracy_score

Read CSV as DataFrame

df = pd . read_csv ( 'movie_review.csv' )

DataFrame preview

<bound method NDFrame.head of fold_id cv_tag html_id sent_id text tag 0 0 cv000 29590 0 films adapted from comic books have had plenty... pos 1 0 cv000 29590 1 for starters , it was created by alan moore ( ... pos 2 0 cv000 29590 2 to say moore and campbell thoroughly researche... pos 3 0 cv000 29590 3 the book ( or " graphic novel , " if you will ... pos 4 0 cv000 29590 4 in other words , don't dismiss this film becau... pos ... ... ... ... ... ... ... 64715 9 cv999 14636 20 that lack of inspiration can be traced back to... neg 64716 9 cv999 14636 21 like too many of the skits on the current inca... neg 64717 9 cv999 14636 22 after watching one of the " roxbury " skits on... neg 64718 9 cv999 14636 23 bump unsuspecting women , and . . . that's all . neg 64719 9 cv999 14636 24 after watching _a_night_at_the_roxbury_ , you'... neg [64720 rows x 6 columns]>

Preparing Data

X = df [ 'text' ] y = df [ 'tag' ]

Vectorize Data

vect = CountVectorizer ( ngram_range = ( 1 , 2 )) X = vect . fit_transform ( X )

Split data into random train and test subsets

X_train , X_test , y_train , y_test = train_test_split ( X , y )

Train Bayesan Model

model = BernoulliNB () model . fit ( X_train , y_train )

Predict

p_train = model . predict ( X_train ) p_test = model . predict ( X_test )

Calculating the Accuracy

Accuracy classification score.

In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.



acc_train = accuracy_score ( y_train , p_train ) acc_test = accuracy_score ( y_test , p_test )

Result

print ( f'Train ACC: { acc_train } , Test ACC: { acc_test } ' ) Train ACC : 0.9564276885043264 , Test ACC : 0.6988875154511743

Notebook

Notebook available on Kaggle: https://www.kaggle.com/davidesantangelo/movie-review-sentiment-analysis