Advancing sentiment classification through a population game model approach.

ABSTRACT: Computational Sentiment Analysis involves the automation of human emotion comprehension by categorizing sentiments as positive, negative, or neutral. In the contemporary digital environment, the extensive volume of social media content presents significant challenges for manual analysis, thereby necessitating the development and implementation of automated analytical tools. To address the limitations of existing techniques, which heavily rely on machine learning and extensive dataset pre-training, we propose an innovative unsupervised approach for sentiment classification. This novel methodology is grounded in game theory concepts, particularly the population game model, offering a promising solution by circumventing the need for extensive training procedures. We extract two textual featur

SUBMITTER: Punetha N 

PROVIDER: S-EPMC11375044 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature