A Study on Internet Annotation Analysis System Using Opinion Mining
- Authors
- Jeong, Yen-Tae; Jeon, Byung-Hoon
- Issue Date
- Jun-2023
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- AI; Comment analysis; Deep learning; LSTM algorithm; Sentiment analysis; Video animation; Webtoon
- Citation
- Lecture Notes in Electrical Engineering, v.1028 LNEE, pp 409 - 413
- Pages
- 5
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Electrical Engineering
- Volume
- 1028 LNEE
- Start Page
- 409
- End Page
- 413
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/19383
- DOI
- 10.1007/978-981-99-1252-0_54
- ISSN
- 1876-1100
1876-1119
- Abstract
- This paper proposes a big data sentiment analysis method and deep learning implementation method to provide a webtoon comment analysis web page for convenient comment confirmation and feedback of webtoon writers for the development of the cartoon industry in the video animation field. In order to solve the difficulty of automatic analysis due to the nature of Internet comments and provide various sentiment analysis information, long short-term memory (LSTM) algorithm, ranking algorithm, and word2vec algorithm are applied in parallel, and actual popular works are used to verify the validity. If the analysis method of this paper is used, it is easy to expand to other domestic and overseas platforms, and it is expected that it can be used in various video animation content fields, not limited to the webtoon field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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- Appears in
Collections - College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

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