Sentiment Analysis and Topic Modelling on Twitter Related to Mobile Legends: Bang Bang Game Using Lexicon-Based, LDA, and SVM

Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and competitive issues. The SVM model achieved a sentiment classification accuracy of 90.57%, and cluster classification reached 85.13%. These findings offer valuable insights into how players engage with the game and reflect the underlying sentiments that influence the perception of gameplay and system updates. Furthermore, the predominance of neutral sentiment suggests opportunities for developers and content creators to enhance emotional resonance and community interaction through more engaging content and responsive design. The effectiveness of the combined methodology demonstrates the potential of integrating lexicon-based techniques with machine learning and topic modelling in analysing social media discourse within gaming communities. Future research is recommended to adopt advanced deep learning techniques, develop domain-specific sentiment lexicons, conduct multilingual sentiment analysis, and perform temporal tracking of community sentiment over time, enabling more dynamic and inclusive assessments of user experience and satisfaction.