References
[1]. Tang, D.T, Qiu, S., & Yuan, X. (2015). Learning Sentiment-specific Word Embedding for Twitter Sentiment Classification. Proceedings of the 24th International Conference on Artificial Intelligence, pages 1351-1357.
[2]. Koren, Y., Bell, R., and Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 30-37.
[3]. Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1631-1642).
[4]. Kim, Y. (2014). “Convolutional Neural Networks for Sentence Classification.” In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746-1751.
[5]. Xu, L., Bing, L., Le, Y., & Zhang, P. (2015). “Attention-based LSTM for Aspect-level Sentiment Classification.” In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 606-615.
[6]. Smith, John. “Affective Analysis of Movies using Deep Learning Techniques.” Journal of Film Studies, vol. 14, no. 3, 2022, pp. 45-67.
[7]. Liang Jun, Chai Yumei, Yuan Huibin, etc. Weibo’s emotional analysis based on deep learning [J]. journal of chinese information, 2014,28(05):155-161.
[8]. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer-supported cooperative work (pp. 175-186). ACM.
[9]. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (pp. 285-295).
[10]. Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).
[11]. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
[12]. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 3111-3119.
[13]. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
[14]. Bridle, J. S. (199). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. Neurocomputing, 2(3), 227-236.
[15]. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58.
Cite this article
Bi,R. (2024). Long-term and short-term memory network based movie comment sentiment analysis. Applied and Computational Engineering,36,150-155.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).
References
[1]. Tang, D.T, Qiu, S., & Yuan, X. (2015). Learning Sentiment-specific Word Embedding for Twitter Sentiment Classification. Proceedings of the 24th International Conference on Artificial Intelligence, pages 1351-1357.
[2]. Koren, Y., Bell, R., and Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 30-37.
[3]. Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1631-1642).
[4]. Kim, Y. (2014). “Convolutional Neural Networks for Sentence Classification.” In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746-1751.
[5]. Xu, L., Bing, L., Le, Y., & Zhang, P. (2015). “Attention-based LSTM for Aspect-level Sentiment Classification.” In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 606-615.
[6]. Smith, John. “Affective Analysis of Movies using Deep Learning Techniques.” Journal of Film Studies, vol. 14, no. 3, 2022, pp. 45-67.
[7]. Liang Jun, Chai Yumei, Yuan Huibin, etc. Weibo’s emotional analysis based on deep learning [J]. journal of chinese information, 2014,28(05):155-161.
[8]. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer-supported cooperative work (pp. 175-186). ACM.
[9]. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (pp. 285-295).
[10]. Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).
[11]. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
[12]. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 3111-3119.
[13]. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
[14]. Bridle, J. S. (199). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. Neurocomputing, 2(3), 227-236.
[15]. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58.