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All Journal Techno.Com: Jurnal Teknologi Informasi Pixel : Jurnal Ilmiah Komputer Grafis CommIT (Communication & Information Technology) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Health Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jiko (Jurnal Informatika dan komputer) Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdi Masyarakat Indonesia Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat ProBisnis : Jurnal Manajemen Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat
Amelia Devi Putri Ariyanto
Universitas Widya Husada Semarang
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Journal : CommIT (Communication

 1 
Impact of Statistical and Semantic Features Extraction for Emotion Detection on Indonesian Short Text Sentences Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul; Setyawan, Arif Fitra
CommIT (Communication and Information Technology) Journal Vol. 19 No. 1 (2025): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v19i1.11680

Abstract

The ability to detect emotions in short texts is crucial because interpreting emotions on platforms like Twitter can offer insight into social trends and responses to specific events. Additionally, examining emotions in product reviews assists companies in comprehending customer sentiment, allowing them to improve the quality of their products and services. Most research on Indonesian language emotion detection utilizes statistical feature extraction, with limited discussion on the impact of both statistical and semantic feature extraction. Thus, the research aims to detect emotions in short texts equipped with an analysis of the impact of statistical and semantic features. Analysis of the impact of statistical and semantic features on short texts is necessary to identify the most effective approaches, improve detection accuracy, and ensure that the developed systems can better handle the variety and complexity of informal language. The data used are a public dataset originating from Twitter texts and product review texts in e-commerce. The research utilizes statistical features such as Term Frequency Inverse Document Frequency (TF-IDF) and semantic features such as Bidirectional Encoder Representations from Transformers (BERT). The evaluation results show that using semantic features significantly improves the performance of emotion detection in short texts by 13–24%. It is higher than using statistical features. Deep Learning (DL) algorithms based on neural networks have also been proven to outperform Machine Learning (ML) algorithms in detecting emotions in short text. The experimental results and outlines show the potential directions for future development.
Co-Authors . Maryamah Agus Z A Agus Zainal Arifin Ari Dina Permana Citra Ari Dina Permana Citra Arif Fitra Setyawan Arif Fitra Setyawan Arif Fitra Setyawan Aryaningrum, Novia ‘Azizah, Lutfiyatul Chastine Fatichah Fari Katul Fikriah Lutfiyatul Wahdah M. Bahrul Subkhi Nanik Suciati Rarasmaya I Rizka W S Salsabiil Hasanah Setyawan, Arif Fitra Yuantari, Maria Goretti Catur Yuhana, Umi Laili