Energy-Valence 모델 적용을 통한 문장 감성 기반 음악 추천 시스템 구현

• 영문명: Implementation of Text Emotion-based Music Recommendation System Employing Energy-Valence Model
• 발행기관: 한국IT서비스학회
• 저자명: 김이슬(Leeseul Kim) 김도영(Doyoung Kim) 신수영(Sooyoung Shin) 백남균(Nam-Kyun Baik) 이재호(Jaeho Lee)
• 간행물 정보: 『한국IT서비스학회지』제24권 제2호, 17~29쪽, 전체 13쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2025.04.30

국문 초록

In this study, we present Feelic, an emotion-based music recommendation application that analyzes users' emotions and recommends customized music. The system classifies the user's diary into five emotions: joy, sadness, anger, anxiety, and neutrality using the KoBERT model, and recommends music that matches the emotions by extracting audio characteristics of various songs using Spotify API. Through K-means clustering, music is divided into 30 clusters and songs belonging to clusters such as user-preferred music are recommended. The application is designed so that users can easily write a diary, analyze emotions, and recommend music accordingly, and aims to manage emotions and improve mental health. To prove the usefulness and effectiveness of Feelic, an emotion analysis system, a music recommendation system, and an application implementation process are described in detail. The results are evaluated with the energy-valence model and presented as reasonable.

목차

1. 서론

2. 연구방법

3. 감성 분석 모델

4. 음악 추천 시스템

5. 시스템 구현 및 기능

6. 실험 및 평가

7. 평가

참고문헌

키워드

Emotional Analysis Recommendation System Unsupervised Learning K-means Application Development