| Abstract |
| This study aimed to gain valuable insights into the performance and user satisfaction of applications (apps) through a thorough analysis of Instagram user reviews collected from Google Play. The study utilized text mining and sentiment analysis techniques and systematically identified emotions and opinions embedded in user reviews to deeply understand the areas of improvement and user experiences of the app. It analyzes how Instagram reviews reflect the diverse experiences of users and how they reveal the strengths and weaknesses of the app. For this purpose, sentiment analysis using the naive Bayes algorithm was conducted, and the results were expected to aid in the improvement of Instagram’s services. In addition, the study aimed to assist developers in better understanding and utilizing user feedback, ultimately contributing to enhanced user satisfaction. This study explored the complex relationship between social media usage patterns and user opinions by seeking ways to provide a better user experience through these insights. |
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| Key Words |
| Sentiment Analysis, Social Media Analysis, Naive Bayes Algorithm, Instagram User Reviews, Text Mining, 감성 분석, 소셜 미디어 분석, 나이브 베이즈 알고리즘, 인스타그램 사용자 리뷰, 텍스트 마이닝 |
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