Support Vector Machine and Convolutional Neural Network Approach to Customer Review Sentiment Analysis

Sentiment Analysis is a branch of Natural Language Processing which intends to identify the sentiment in the data being analyzed through polarity analysis and emotion analysis. It can be performed with approaches which are based on Machine Learning as well as the approaches which are based on Lexicons which in turn rely on corpus and dictionaries. Thus, the unprecedented growth of E-Commerce Platforms resulting in an exponential increase in the amount of customer reviews prompts us to choose the most appropriate and optimized models of Sentiment Analysis to produce high accuracy. Since the customer reviews are one of the most important factors which influence the brand value, advertising and customer services of a company, harnessing Sentiment Analysis to get more insight into the reviews is the need of the hour. In this paper, the Customer Review Sentiment Analysis for polarity classification has been performed using Support Vector Machine Model and Convolutional Neural Network Model on a real world dataset of web scraped customer reviews, following which the Support Vector Machine Model was deployed to a web application. By choosing the most appropriate methods of dataset cleaning, text preprocessing and hyper parameter tuning, the Support Vector Machine and the Convolutional Neural Network Model achieved high accuracies of 96% and 94% respectively. Thus, both the Sentiment Analysis models have achieved much higher accuracy and minimal error rate in contrast to the existing models.

I. Introduction

Sentiment Analysis also referred to as Opinion Mining can be defined as the evaluation of data to perform polarity analysis and emotion analysis using Natural Language Processing. Polarity analysis enables us to categorize the sentiment behind the text as positive, negative or neutral while emotion analysis further intends to characterize the exact feelings of the opinion holder such as fear, happiness, anger, hatred, disgust and so on.