Game Sentiment Prediction Using Deep Learning Regression

Chettri, Aryaman (2025) Game Sentiment Prediction Using Deep Learning Regression. Masters thesis, Dublin, National College of Ireland.

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Abstract

Gaming has evolved from paying for a physical game to monetisation ecosystems comprising microtransactions, battle passes, loot boxes, and gacha mechanics, and many of these systems have been publicly criticised for exploiting players. Steam reviews and Reddit threads are usual spots for feedback on games and game developers. Comparing the performance of deep learning regression models for predicting video games' sentiment scores based on independent game metadata features. Data included Reddit (posts, upvotes, comments), price, platform and downloadable content. Data for 1,413 games were retrieved using the Steam Web API and the Reddit API. 27 GDPR-compliant features were devised. The four architectures tested include LSTM, CNN, DNN and the hybrid model. Ground truth sentiment scores have been computed using VADER for Steam reviews. LSTM's MAE was 0.130, R² score 0.043, with a (-0.5, 0.8) range. This was 2.2% better than baseline and achieved the highest correlation among the 5 models (0.174 between features), indicating that the features were independent of one another. The resulting positive R² of 4.3% suggests that there is a useful signal in the metadata and a baseline performance suitable for production, with low sentiment scores correlated with excessive downloadable content, predatory free-to-play models, and controversy.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure > Games and Amusements Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning G Geography. Anthropology. Recreation > GV Recreation Leisure > Games and Amusements > Online Games
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 Sep 2026 09:35
Last Modified:	07 Sep 2026 09:35
URI:	https://norma.ncirl.ie/id/eprint/9851

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