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All Journal Pixel : Jurnal Ilmiah Komputer Grafis RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JIKO (Jurnal Informatika dan Komputer) Jurnal Ilmu Komputer dan Desain Komunikasi Visual Prosiding National Conference for Community Service Project Jurnal AKMAMI (Akuntansi Manajemen Ekonomi) Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknimedia: Teknologi Informasi dan Multimedia Infotech: Jurnal Informatika & Teknologi Judikatif: Jurnal Desain Komunikasi Kreatif Journal of Information System and Technology (JOINT) TIERS Information Technology Journal Jurnal Teknologi Komputer dan Informasi Journal Technology and Industrial Engineering Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Business, Social Sciences and Technology (CoNeScINTech)
Syahputra, Bayu
Universitas Internasional Batam
Aerospace Engineering Arts Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Genre-Based Sentiment and Emotion System for Audience Insight Suwarno, Suwarno; Wesly, Wesly; Syahputra, Bayu
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7183

Abstract

Movies can influence people’s moods in different ways depending on film genre. Fear is commonly induced by horror films, whereas joy is typically associated with comedy. Understanding how genre-based expectations shape audience emotions offers valuable insights for producers and digital platforms. However, previous studies have only briefly examined this relationship, with most focusing on general sentiment analysis. This study develops a genre-based sentiment and emotion model to analyze how film genres influence audience reactions. The Cross-Industry Standard Process for Data Mining (CRISP-DM) framework was applied to 46,173 IMDb reviews using Term Frequency–Inverse Document Frequency (TF-IDF) features and three machine learning models: Logistic Regression, Linear Support Vector Classification, and One-vs-Rest Logistic Regression. The results show that Fear (0.704) and Anger (0.684) are the most dominant emotions, indicating that audiences tend to be more emotionally engaged with intense genres. The model was also implemented in a Flask–React web-based system that allows users to analyze and visualize reviews in real time. These findings provide practical implications for filmmakers, producers, and streaming platforms in adjusting genre design, content recommendation, and promotional strategies to align with audience emotional responses.
Co-Authors Adi Adi Adi Aguspriyanti, Carissa Dinar Alifa Nadaa Samiyyah Andrian, Jason Antony Antony Anweidri Chai Bryan Tandian Bryan Tandian Charline Marshella Ong Christiana Christiana Christine Gabriella Christine Gabriella Crystina Crystina Deli Deli Deli Deli Deli Della Hervi Syahputri Dinna Theresia Kalalo Edmund Edmund Egnes Egnes Eryc, Eryc Evans, Felix Febby Anggellya Fredian Simanjuntak Handyca, Handyca Hendi Che Ika, Nadia Indasari Deu Julvina Julvina Kelvianto Kelvianto Kusnan Kusnan Lesilolo, Leona Stephanie Lijaputra , Delphy Livianti, Fiona Marcellino, Marcellino maria, dulcie Marikena, Nita Mayliska Tandi Muhamad Dody Firmansyah Mungkap Mangapul Siahaan Natasha Dwi Wulandari Navelina, Navelina Nicholas Nicholas Nicki Carter Nur Fatihah Osei Enoch Aboagye Pratama, Jimmy Raymond Reynaldi Arsani Runtu Rudi Chandra Sabariman Sabariman Sabariman Sabariman Said Teguh Putra Ramadhan Sama, Hendi Sellyana, Ari Seprida Hanum Harahap, Seprida Hanum Stephanie Steven Marlin Suwarno Suwarno Tjahyadi, Surya Tony Wibowo, Tony Tresia Togi Mariboto br Manik Vinson Vinson Vinson, Vinson Vivi Dominica Chai Vladimir Lim Wanda Fatika Sari Welly Desriyati Wesly, Wesly Wibowo, Tony William, Charles Wilson, Kenny Winson Winson Zaspin Zulkarnain Zulkarnain Zulkarnain