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All Journal International Journal of Health, Engineering and Technology
Rizalina Rizalina
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Dentistry Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Sentiment Analysis on Twitter Social Media towards Najwa Shihab Using Naïve Bayes Algorithm and Support Vector Machine (SVM) Fahruzi Sirait; Desi Irpan; Riszki Fadillah; Rizalina Rizalina; Riswan Syahputra Damanik
International Journal of Health Engineering and Technology Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.280

Abstract

With the rapid growth of digital technology, social media has become a key platform for sharing information and opinions. Twitter, one of the most popular platforms in Indonesia, enables users to interact directly with public figures such as Najwa Shihab. This study aims to analyze public sentiment toward Najwa Shihab on Twitter using sentiment analysis, specifically employing the Naïve Bayes and Support Vector Machine (SVM) algorithms. Sentiment analysis is essential to understanding public opinion, as it classifies text into categories like positive, negative, or neutral, providing valuable insights into societal perspectives on public figures. In this study, 10,000 tweets related to Najwa Shihab were collected from January 1, 2023, to January 31, 2023. Data preprocessing steps such as data cleaning, tokenization, stopwords removal, and filtering were conducted to ensure high-quality data for analysis. The Naïve Bayes and SVM algorithms were applied using RapidMiner to classify the sentiment of the tweets. The performance of both algorithms was evaluated based on accuracy, precision, recall, and F1-score.The results revealed that SVM outperformed Naïve Bayes in all metrics, demonstrating its superior ability to classify sentiments correctly. The sentiment distribution indicated a majority of positive opinions toward Najwa Shihab, with fluctuations in negative sentiment during specific events. This study provides insights into public sentiment analysis and contributes to understanding social media opinions on public figures.
Co-Authors Desi Irpan Fahruzi Sirait Riswan Syahputra Damanik Riszki Fadillah