Public Sentiment Analysis on the Transition from Analog to Digital Television Using the Random Forest Classifier Algorithm
Abstract
Television is one of the most popular media for entertainment and information. Analog television is the most widely used type among the public. However, with technological advancements, analog television is becoming obsolete and is being replaced by digital television, which offers better performance. On November 2, 2022, the Government officially mandated the transition from analog to digital broadcasting. This Analog Switch Off program has elicited various pro and con opinions among the public. Twitter, a widely used social media platform, facilitates rapid communication and information dissemination among users. This study aims to classify public sentiment regarding the Analog Switch Off policy as either positive or negative. The classification model used is the Random Forest algorithm, with the Lexicon Inset for data labeling, Count Vectorizer and TF-IDF Vectorizer for data vectorization and weighting, and various train-test data splits. The study achieved the best classification performance using the Count Vectorizer method, with an 80%:20% train-test data ratio, yielding an accuracy of 88%, precision of 88%, recall of 88%, and an F1-score of 88%.
Index Terms—Analog Television; Digital Television; Sentiment; Twitter; Random Forest
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