Topic modeling in game reviews

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this thesis, Metacritic game reviews for Elden Ring were analyzed for topic modelling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid model combining both to identify effective methods for extracting underlying themes in player feedback. We analyzed and interpreted these models’ outputs to learn the game reviews. We aimed to identify the differences, similarities, and variations between the three to determine which provided more valuable and instructive information. Our findings indicate that each method successfully identified keywords with some similarities in identified words. The LDA model had the highest silhouette score, indicating the most distinct clustering. The LDA-BERT model had a 1% higher coherence score than LDA, indicating more meaningful or relevant topics.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Dehghani, Fatemeh

Advisor dc:contributor.advisor

• Zaman, Loutfouz

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1923

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1923