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All Journal CommIT (Communication & Information Technology) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research Jurnal Info Sains : Informatika dan Sains Jurnal Armada Informatika Journal of Computing Theories and Applications
Octara Pribadi
Sekolah Tinggi Manajemen Informatika Dan Komputer (STMIK) TIME
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

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Comprehensive Comparison of TF-IDF and Word2Vec in Product Sentiment Classification Using Machine Learning Models Sinaga, Asra Gretya; Robet, Robet; Pribadi, Octara
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11582

Abstract

Sentiment analysis supports data-driven decisions by turning product reviews into reliable polarity labels. We compare four text representations, TF-IDF, TF-IDF reduced via SVD, Word2Vec (trained from scratch), and a hybrid TF-IDF(SVD-300). Word2Vec, for sentiment classification of Indonesian Shopee product reviews from Kaggle (~2.5k texts). After normalization (with optional emoji handling and Indonesian stemming), ratings are mapped to binary sentiment (≤2 negative, ≥4 positive; 3 discarded). Each representation is evaluated with Logistic Regression, Support Vector Machines (linear/RBF), Naive Bayes, and Random Forest under stratified 5-fold cross-validation. TF-IDF with Logistic Regression (C=1.0) yields the best results (F1-macro = 0.816 ± 0.026; Accuracy = 0.816 ± 0.026), with LinearSVC as a strong runner-up. Word2Vec (scratch) performs lower, consistent with limited data being insufficient to learn stable embeddings, while the hybrid representation offers only modest gains over Word2Vec and does not surpass TF-IDF. These findings indicate that TF-IDF is the most reliable and consistent representation for small, short-text review datasets, and they underscore the impact of feature design on downstream classification performance.
Co-Authors Aghaunor, Tabitha Chukwudi Ali, Rabei Raad Andi Andi Carles Juliandy Daniel Happy Putra david De Rosal Ignatius Moses Setiadi Destisonya Buulolo Edi Edi Wijaya ENDRI ENDRI Feriani Astuti Tarigan Halim, Marwa Hendri Hendri Hendri Hendri Hoki, Leony Jackri Hendrik Jackri Hendrik Johanes Terang Kita Perangin Angin Juan Charles Juliyanto Kartikadarma , Etika Ojugo, Arnold Adimabua Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Pieter Octaviandy, Pieter Rizkita, Ari Robert Robet Robet Robet rusdiana Sarker, Md Kamruzzaman Setyoko, Bimo Haryo Sinaga, Asra Gretya Sukiman Sukiman Suwanty Suwanty Suwanty Suwanty, Suwanty Suyud Widiono Syamsiar, Syamsiar Teta, Bayu Tio, Nicholas Ugbotu, Eferhire Valentine Wilson