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All Journal Jurnal Komtika (Komputasi dan Informatika) Metalurgi El-Fata: Journal of Sharia Economics and Islamic Education Journal of Embedded Systems, Security and Intelligent Systems IWTJ : International Water Transport Journal JITAA : Journal Of International Taxation, Accounting And Auditing Galaksi Jurnal Komtika (Komputasi dan Informatika)
Hendrik Setiawan, Hendrik
Jurusan Teknik Elektro Universitas Surabaya, Surabaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Social Sciences Transportation

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Performance Analysis of SVM In Emotion Classification: A Comparative Study Of TF-IDF and Countvectorizer Fitriana, Frizka; Setiawan, Hendrik
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8396

Abstract

In today’s digital era, emotion analysis of social media comments plays a critical role in gaining deeper insights into user sentiment. This study aims to compare two text representation methods TF-IDF and CountVectorizer in enhancing the performance of the Support Vector Machine (SVM) algorithm for emotion classification. The dataset employed in this research is a subset of GoEmotions, consisting of 1,000 YouTube comments labeled with 27 distinct emotion categories. The dataset was split into training and testing sets with an 80:20 ratio. Both text representation methods were tested separately using a linear kernel in the SVM algorithm. The models were evaluated based on accuracy, precision, recall, and F1-score. The classification results show that TF-IDF slightly outperformed CountVectorizer in terms of accuracy (35% vs. 32%). However, CountVectorizer exhibited marginally better performance in precision and F1-score. These findings suggest that the choice of text representation significantly impacts emotion classification outcomes. This research contributes to the development of text-based emotion analysis systems for social media platforms.
Co-Authors Abdul Latif Bratakusuma, Danu Darmawangsa, Andi Ema Utami Eny Latifah, Eny Febriansyah Febriansyah Fitriana, Frizka Himawan Tri Bayu Murti Petrus Lawang, Hasanna Mamdukh, Muhammad Mubarriroh, Lailatul Oktafanda, Ego Rohmah, Yuni Santosa, Firman Sudarmawan, Sudarmawan Sutrisno, Slamet Prasetyo Widi Astuti Wijaya, Ardyanto Wiratni Wiratni, Wiratni