Aspect-Based Sentiment Analysis on Movie Reviews

Abstract

Voice of the Customer (VoC) has gained traction over the past few years to understand the consumers' opinion, preferences, and expectation. Reviews that are posted online are one of the methods of communication between the company and the consumers. Therefore, companies can analyse the reviews posted online to identify the aspects and sentiments that are mentioned in the reviews. However, the process of analysing the reviews manually is inefficient and is prone to bias. One of the methods of tackling manually analysing is by using machine learning. This process is called aspect-based sentiment analysis, there are many aspect-based sentiment analysis studies and research has been done previously. However, majority of the previous studies focuses on other domains such as product reviews or restaurant reviews. Therefore, this research will focus on the movie industry where movie reviews will be used to train and predict the aspects and sentiment of the movie review using machine learning models. This research will perform both aspect prediction and sentiment prediction on different models. The aspect prediction will be done using Logistic Regression and Decision Tree whist the Sentiment Analysis will be done using Logistic Regression and Multinomial Naïve Bayes. Based on the findings of the study, Decision Tree was able to achieve a higher accuracy of 98% while Logistic Regression was able to score an accuracy of 92%. Additionally, Logistic Regression was able to score a better accuracy for Sentiment Prediction with an accuracy of 93% when compared to Multinomial Naïve Bayes which achieved an accuracy of 91 %. Therefore, Decision Tree is more suitable for Aspect Prediction whilst Logistic Regression is more suitable for Sentiment Analysis.