KLASIFIKASI DATA TEKS UNTUK MENDETEKSI EMOSI PENGGUNA TWITTER MENGGUNAKAN MACHINE LEARNING
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Abstract
Emotion detection on social media platforms such as Twitter is a challenge in the world of data processing. In contrast to real life, detecting emotions in text is a complex task due to a lack of context or some sentences are not provided with sufficient context to understand the emotions contained therein. This research aims to create a model that is capable of meeting the needs of text-based emotion classification in the Twitter application. In this research, a comparison was carried out between the Naïve Bayes and LinearSVC algorithms, and the final results were obtained where the LinearSVC algorithm achieved the best accuracy with 66%, while the Naïve Bayes algorithm achieved 57% accuracy. By conducting this research, it is hoped that it can contribute to developing understanding of sentiment analysis on social media and open up opportunities for further applications, such as monitoring public opinion and analyzing emotional trends in real-time.
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