• S. Prabhas
• Dr. N. Rahul Pal
• D. Susmitha

One of the main areas of natural language processing is sentiment analysis, which is used to identify whether input is neutral, positive, or negative. It is frequently used on textual data to assist companies in tracking the sentiment of their brands and products in customer reviews in order to better understand the demands of their clientele. In order to create sentiment analysis models specifically for the gaming industry, this study investigates a variety of machine learning methods, primarily the Logistic Regression (LR) Algorithm, which may categorize evaluations as neutral, negative, or positive. Game reviews from Steam and Metacritic were used to train the models. The models' performance was assessed and contrasted using a variety of data pretreatment and model optimization strategies.

Algorithms for Logistic Regression, Machine Learning, and Sentiment Analysis

"Sentiment Analysis On Online Gaming Reviews Based On Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.e269-e277, January-2025, Available :http://www.jetir.org/papers/JETIR2501531.pdf

"Sentiment Analysis On Online Gaming Reviews Based On Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppe269-e277, January-2025, Available at : http://www.jetir.org/papers/JETIR2501531.pdf