Abstract
Studies have shown how social networking sites have been used in the political landscape as a tool to disseminate information, influence people in their political views and voting decisions, and even predict election results. This study analyzes voter preferences and identifies the topics of discussion on 2022 election-related tweets using sentiment analysis and topic modelling. Naive Bayes and Support Vector Machine are used for the sentiment analysis classifier models and Biterm Topic Modeling for identifying the most discussed topics. The results of sentiment analysis show that the Naive Bayes classifier gained a higher accuracy score of 73% than Support Vector Machine with 69%. By focusing on the leading presidential candidates, the sentiment classification revealed that Leni Robredo obtained higher positive sentiment rating than Bongbong Marcos, and is the most tweeted candidate. Significant issues regarding the candidates and the elections are determined from the topic models.
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