Sentiment based anime recommendation system - Wong, Tze-Qing, Sarah (2024)
Anime has grown to be a vibrant and significant subset of the entertainment industry with its distinct fusion of narrative, artistic expression, and cultural impact. The problem identified in this report is the lack of personalization in anime recommendations by existing anime related platforms. Therefore, an innovative anime recommendation mobile application is proposed to overcome the issue. Sentiment analysis is focused on in this project to develop the recommendation system to revolutionize how users find and interact with anime content which will improve their viewing experience. An elaborate explanation regarding the problem of lack of personalization is discussed in this proposal as well. Sufficient reviews of the existing anime related platforms are presented and compared to further investigate the mentioned problem. Furthermore, the scope and objectives of this project are identified. The techniques to be used include web crawling and sentiment analysis powered by neural network. This proposal includes the system design of the mobile application to further elaborate the proposed solution too.