Abstract
Sentiment analysis is one of the major topics of natural language processing which is used to determine whether data is positive, negative or neutral. It is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback to understand their customers’ needs. This paper explores various machine learning algorithms including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP) and Extreme Gradient Boosting Classifier (XGB) to build sentiment analysis models tailored for the gaming domain to classify reviews into positive, negative and neutral. The models were trained on game reviews obtained from Metacritic and Steam. Various data preprocessing and model optimization techniques have been employed and the performance of the models were evaluated and compared. SVC has been determined as the best-performing model among all the models.
Keywords: Sentiment Analysis, Natural Language Processing, Machine Learning, Support Vector Machine, Game Reviews
Authors
Tan Jie Ying [1] ; Andy Chow Sai Kit [2] ; Tan Chi Wee [3]
[1][2][3] Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[1] tanjy-wp17@student.tarc.edu.my
Cite Me
Plain Text:
J.Y.Tan, C.S.K.Andy, C.W.Tan, "Sentiment Analysis on Game Reviews: A Comparative Study of Machine Learning Approaches," International Conference on Digital Transformation and Applications (ICDXA) 2021, 2021, pp. 209-216, doi: https://doi.org/10.56453/icdxa.2021.1023.
BibTex:
@INPROCEEDINGS{ICDXA202123,
author={Tan, Jie Ying and Andy, Chow Sai Kit and Tan, Chi Wee},
booktitle={International Conference on Digital Transformation and Applications (ICDXA) 2021},
title={Sentiment Analysis on Game Reviews: A Comparative Study of Machine Learning Approaches},
year={2021},
volume={},
number={},
pages={209-216},
doi={https://doi.org/10.56453/icdxa.2021.1023}}
