Analysis of public sentiment towards the Tiktok application using the Naive Bayes Algorithm and support Vector machine.

Hidayah, Ika Arofatul, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219, Abidin, Zainal ORCID: https://orcid.org/0000-0002-9261-4952 and Imamudin, Mochamad (2024) Analysis of public sentiment towards the Tiktok application using the Naive Bayes Algorithm and support Vector machine. Journal of Computer Networks, Architecture and High Performance Computing, 6 (2). pp. 881-891. ISSN 2655-9102

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Abstract

In the current digital era, social media applications such as TikTok have become an important aspect of people's lives. TikTok allows users to create and share short videos, making it a global phenomenon with millions of active users. However, this application has also been the subject of various responses and opinions from the public. This research aims to classify public sentiment towards the TikTok application based on comments on Playstore using the Naïve Bayes algorithm and Support Vector Machine (SVM). This research method involves collecting comment data from Playstore using scraping techniques, resulting in 5,000 review data. Data pre-processing stages include case folding, tokenization, normalization, stopword removal, stemming, and data labeling using a lexicon. The data that has been processed is then weighted using Term Frequency - Inverse Document Frequency (TF-IDF) before being classified using the Naïve Bayes and SVM algorithms. Algorithm performance evaluation is carried out using the Confusion Matrix to measure accuracy, precision and recall. The research results show that the SVM algorithm has higher accuracy (84%) compared to Naïve Bayes (79%). SVM also shows better precision and recall values in classifying positive and negative sentiments from user reviews. From the results of the tests that have been carried out, the SVM algorithm is more effective than Naïve Bayes in sentiment analysis of the TikTok application. This research provides insight into how public sentiment can be measured and analyzed and underscores the importance of choosing the right algorithm for data sentiment analysis on social media platforms.

Item Type:	Journal Article
Keywords:	Sentiment Analysis; Naïve Bayes; Support Vector Machine; TikTok
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080699 Information Systems not elsewhere classified 08 INFORMATION AND COMPUTING SCIENCES > 0807 Library and Information Studies > 080709 Social and Community Informatics 08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Ririen Kusumawati
Date Deposited:	27 Aug 2024 14:31

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