Genre-Based Sentiment and Emotion System for Audience Insight

Suwarno Suwarno; Wesly Wesly; Bayu Syahputra

doi:10.38043/tiers.v6i2.7183

Authors

Suwarno Universitas Internasional Batam, Indonesia
Wesly Universitas Internasional Batam, Indonesia
Bayu Syahputra Universitas Internasional Batam, Indonesia

DOI:

https://doi.org/10.38043/tiers.v6i2.7183

Keywords:

genre, sentiment, emotion, machine learning, CRISP-DM

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.

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Author Biographies

Wesly, Universitas Internasional Batam, Indonesia

Information System Study Program

Bayu Syahputra, Universitas Internasional Batam, Indonesia

Information System Study Program

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