EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 157 (2023) SHS Web Conf., 157 (2023) 04012 References
Open Access
Issue
SHS Web Conf.
Volume 157, 2023
2022 International Conference on Educational Science and Social Culture (ESSC 2022)
Article Number 04012
Number of page(s) 4
Section Human Behavioural Science and Social Development
DOI https://doi.org/10.1051/shsconf/202315704012
Published online 13 February 2023
  1. Hu, M. and B. Liu. Opinion extraction and summarization on the web. in Aaai. 2006. [Google Scholar]
  2. Yu, H. and V. Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. in Proceedings of the 2003 conference on Empirical methods in natural language processing. 2003 [Google Scholar]
  3. Lee, S.H., J. Cui, and J.W. Kim, Sentiment analysis on movie review through building modified sentiment dictionary by movie genre. Journal of intelligence and information systems, 2016. 22(2): p. 97–113. [Google Scholar]
  4. Ahmed, E., et al. Challenges, comparative analysis and a proposed methodology to predict sentiment from movie reviews using machine learning. in 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). 2017. IEEE. [Google Scholar]
  5. Hu, Z., et al. Review sentiment analysis based on deep learning. in 2015 IEEE 12th International Conference on e-Business Engineering. 2015. IEEE. [Google Scholar]
  6. Wang, J. and Z. Cao. Chinese Text Sentiment Analysis Using LSTM Network Based on L2 and Nadam. [Google Scholar]
  7. Pang, B., L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment Classification using Machine Learning Techniques. 2002. Association for Computational Linguistics. [Google Scholar]
  8. Mullen, T. and N. Collier. Sentiment analysis using support vector machines with diverse information sources. in Proceedings of the 2004 conference on empirical methods in natural language processing. 2004. [Google Scholar]
  9. Socher, R., et al. Recursive deep models for semantic compositionality over a sentiment treebank. in Proceedings of the 2013 conference on empirical methods in natural language processing. 2013. [Google Scholar]
  10. Zhou, S., Q. Chen, and X. Wang, Active deep learning method for semi-supervised sentiment classification. Neurocomputing, 2013. 120: p. 536–546. [Google Scholar]
  11. Marcec, R. and R. Likic, Using twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgraduate Medical Journal, 2022. 98(1161): p. 544–550. [Google Scholar]
  12. Somula, R., et al., Twitter sentiment analysis based on US presidential election 2016, in smart intelligent computing and applications. 2020, Springer. p. 363–373. [Google Scholar]
  13. Ibar-Alonso, R., R. Quiroga-García, and M. Arenas-Parra, Opinion Mining of Green Energy Sentiment: A Russia-Ukraine Conflict Analysis. Mathematics, 2022. 10(14): p. 2532. [Google Scholar]
  14. Hao, K., Research on the Event Forecast with Text Sentiment Orientation Classification [D]. Changsha: National University of Defence Technology, Doha, Qatar, 2012. [Google Scholar]
  15. Su, Q., et al. Hidden sentiment association in chinese web opinion mining. in Proceedings of the 17th international conference on World Wide Web. 2008. [Google Scholar]
  16. Zhang, T. and J. Guo. Research on Chinese Sentiment Analysis Based on Bi-LSTM Networks. in IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall). 2021. IEEE. [Google Scholar]
  17. Mikolov, T., et al., Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. [Google Scholar]
  18. Pennington, J., R. Socher, and C.D. Manning. Glove: Global vectors for word representation. in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014. [Google Scholar]
  19. Hochreiter, S. and J. Schmidhuber, Long short-term memory. Neural computation, 1997. 9(8): p. 1735–1780. [Google Scholar]
  20. Hochreiter, S., The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998. 6(02): p. 107–116. [CrossRef] [Google Scholar]
  21. Graves, A. and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 2005. 18(5–6): p. 602–610. [Google Scholar]
  22. Islam, M.S. and E. Hossain, Foreign exchange currency rate prediction using a GRU-LSTM hybrid network. Soft Computing Letters, 2021. 3: p. 100009. [Google Scholar]
  23. Zhang, K., et al. Sentiment Analysis of Chinese Product Reviews Based on BERT Word Vector and Hierarchical Bidirectional LSTM. in 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE). 2021. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.