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All Journal Jurnal Riset Kimia JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Knowbase : International Journal of Knowledge in Database SAINSTEK Jurnal Informatika Ekonomi Bisnis
Widya Wahyuni
Fakultas Ilmu Komunikasi
Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : Knowbase : International Journal of Knowledge in Database

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Implementation of the Support Vector Machine Method for Sentiment Analysis Using Twitter Data Wahyuni, Widya
Knowbase : International Journal of Knowledge in Database Vol. 2 No. 2 (2022): December 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v2i2.6019

Abstract

The development of feminism, which is centered on women all over the world who want to be free of male pressure, oppression, and inequality, has continued to the present day. Various public opinions about feminism are now being expressed on various social media platforms. There has been a long debate about feminism's critics and supporters in terms of equalizing women's intellectual and the role of women in making decisions. Not only that, but the desire to end acts of violence and injustice against women is a form of feminism that is often taken for granted, even in the legal realm. The purpose of this study was to examine public sentiment based on opinions shared on social media. Hashtags related to feminism from social media are the main data that will be used to analyze public opinion sentiments about feminism. In this study, 500 tweets were used, and the data was later separated into positive, negative, and neutral opinions before being analyzed using the Support Vector Machine (SVM) method. The results of this study obtained an accuracy of 72%, indicating that the use of SVM to perform sentiment analysis on Twitter data is quite good.
Co-Authors -, Mulyazmi -, Zulfiandriana Apriliana, Milla Ermiyati Eva Oktavia, Eva fakhri Gumelta, Riyang Gussyafri Ismalia, Welly Novan, Andre Praputri, Erti Safridatul Audah Tri Putri Lestari, Tri Putri Yenita Morena