Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques

TL;DR

This paper develops sentiment analysis models for IMDB movie comments and Twitter data using machine learning and vector space techniques, comparing the performance of Decision Trees, Naive Bayes, and SVM algorithms.

Contribution

It applies and compares multiple machine learning algorithms for sentiment classification on two different social media datasets using vector space models.

Findings

SVM achieved the highest accuracy on both datasets.

Classification accuracy was 94% for IMDB comments with Decision Tree.

Twitter data classification accuracy was up to 82.76% with Decision Tree.

Abstract

This study's goal is to create a model of sentiment analysis on a 2000 rows IMDB movie comments and 3200 Twitter data by using machine learning and vector space techniques; positive or negative preliminary information about the text is to provide. In the study, a vector space was created in the KNIME Analytics platform, and a classification study was performed on this vector space by Decision Trees, Na\"ive Bayes and Support Vector Machines classification algorithms. The conclusions obtained were compared in terms of each algorithms. The classification results for IMDB movie comments are obtained as 94,00%, 73,20%, and 85,50% by Decision Tree, Naive Bayes and SVM algorithms. The classification results for Twitter data set are presented as 82,76%, 75,44% and 72,50% by Decision Tree, Naive Bayes SVM algorithms as well. It is seen that the best classification results presented in both data sets are which calculated by SVM algorithm.