Leveraging the OPT Large Language Model for Sentiment Analysis of Game Reviews

Abstract

Automatically extracting players' sentiments about games can help game developers to better understand the aspects of their games that players like or dislike. Our prior work showed that traditional sentiment analysis techniques do not perform well on game reviews. However, the natural language processing field has seen a steep progress in recent years. In this letter, we follow up on our prior work and investigate how a state-of-the-art large language model (OPT-175B) performs on the sentiment classification of game reviews. We manually analyze the game reviews wrongly classified by OPT-175B to better understand the issues that affect the performance of that model and how those issues compare to the challenges faced by traditional classifiers. We found that OPT-175B achieves (far) better performance than traditional sentiment classifiers, with a 72%-increased $F$ -measure and a 30%-increased AUC compared to the best traditional classifier studied in our prior work. We also found that common challenges of traditional classifiers, such as reviews with game comparisons and negative terminology, have been mostly solved by the OPT-175B model.