Sentiment Analysis of Game Reviews and Hyper Parameter Tuning of the Model

Abstract

The Gaming Industry is widely exposed to experimental products and it is uncertain for the customer to choose a better game. Sentimental analysis is the process by which the emotional tone behind a number of words can be determined so that attitudes, opinions, and emotions expressed on the website are understood. In the proposed method, Machine Learning models are used to perform sentiment analysis on STEAM game reviews. Neural Networks is used be perform sentiment Analysis. Optimal Hyperparameters for training the model are selected using the newly proposed nature inspired algorithm, Harris Hawks Optimization Algorithm.