Open Access
| Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
|
|
|---|---|---|
| Article Number | 03006 | |
| Number of page(s) | 7 | |
| Section | Blockchain, AI, and Technology Integration | |
| DOI | https://doi.org/10.1051/itmconf/20257303006 | |
| Published online | 17 February 2025 | |
- Y. Zhang, J. Sun, L. Meng, et al., Sentiment analysis of E-commerce text reviews based on sentiment dictionary, in Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), IEEE, (2020), 1346-1350 [Google Scholar]
- K. Jia, Z. Li, Chinese micro-blog sentiment classification based on emotion dictionary and semantic rules, in Proceedings of the 2020 International Conference on Computer Information and Big Data Applications (CIBDA), (2020), 309-312 [Google Scholar]
- S. Bagherzadeh, S. Shokouhyar, H. Jahani, et al., A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews. J. Hosp. Tour. Technol. 12(2), 210-238 (2021) [Google Scholar]
- H. Rahman, J. Tariq, M. A. Masood, et al., Multi-tier sentiment analysis of social media text using supervised machine learning, Comput. Mater. Contin. 74, 5527–5543 (2023) [Google Scholar]
- H. Cam, A. V. Cam, U. Demirel, et al., Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers, Heliyon, 10(1), e23784 (2024) [Google Scholar]
- S. H. Biradar, J. V. Gorabal, G. Gupta, Machine learning tool for exploring sentiment analysis on Twitter data, Mater. Today: Proc. 56, 1927–1934 (2022) [CrossRef] [Google Scholar]
- N. Pavitha, V. Pungliya, A. Raut, et al., Movie recommendation and sentiment analysis using machine learning, Global Transitions Proc. 3(1), 279–284 (2022) [CrossRef] [Google Scholar]
- M. E. Basiri, S. Nemati, M. Abdar, et al., ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis, Future Gener. Comput. Syst. 115, 279–294 (2021) [CrossRef] [Google Scholar]
- A. Assiri, A. Gumaei, F. Mehmood, et al., DeBERTa-GRU: Sentiment analysis for large language model, Computers, Mater. & Contin. 79(3), 21517-21525 (2024) [Google Scholar]
- M. Rhanoui, M. Mikram, S. Yousfi, et al., A CNN-BiLSTM model for document-level sentiment analysis, Mach. Learn. Knowl. Extract. 1(3), 832–847 (2019) [CrossRef] [Google Scholar]
- W. Liao, B. Zeng, X. Yin, et al., An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa, Appl. Intell. 51, 3522–3533 (2021) [CrossRef] [Google Scholar]
- H. Hu, M. Feng, M. Cao, et al., Multimodal social sentiment analysis based on semantic relevance, J. Beijing Univ. Aeronaut. Astronaut. 47(3), 469–477 (2021) [Google Scholar]
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