Comprehensive exploration of game reviews extraction and opinion mining using nlp techniques

Abstract

Sentiment analysis and opinion summarization have become an important research area with the increase of available data on the Web. Since the Internet started containing more and more opinions and reviews for different products, individual users and companies saw the benefits of a priori evaluations based on other users’ experiences; thus, automated analyses centered on customer impressions and experiences emerged as crucial marketing instruments. Our aim is to create a scalable and easily extensible pipeline for building a custom-tailored sentiment analysis model for a specific domain. A corpus of around 200,000 games reviews was extracted, and three state-of-the-art models (i.e., support vector machines, multinomial Naïve-Bayes, and deep neural network) were employed in order to classify the reviews into positive, neutral, and negative. Current results surpass previous experiments based on word counts applied on a similar game reviews dataset, thus arguing for the adequacy of the proposed workflow.