References
- Agarwal, A., Yadav, A., and Vishwakarma, D. K., "Multimodal sentiment analysis via RNN variants," 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD), IEEE, 2019.
- Ambartsoumian, A. and Popowich, F., "Self-attention: A better building block for sentiment analysis neural network classifiers," arXiv preprint arXiv:1812.07860, 2018.
- Bahdanau, D., Cho, K., and Bengio, Y., "Neural machine translation by jointly learning to align and translate," arXiv preprint arXiv:1409.0473, 2014.
- Baltrusaitis, T., Ahuja, C., and Morency, L., "Multimodal machine learning: A survey and taxonomy," IEEE transactions on pattern analysis and machine intelligence, Vol. 41, No. 2, pp. 423-443, 2018. https://doi.org/10.1109/tpami.2018.2798607
- Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014.
- Devlin, J., Chang, M., Lee, K., and Toutanova, K., "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018.
- Gruber, N. and Jockisch, A., "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?," Frontiers in Artificial Intelligence, Vol. 3, 2020.
- Hochreiter, S. and Schmidhuber, J., "Long short-term memory," Neural Computation, Vol. 9. No. 8, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
- Huang, Q., Chen, R., Zheng, X., and Dong, Z., "Deep sentiment representation based on CNN and LSTM", 2017 International Conference on Green Informatics (ICGI), IEEE, 2017.
- Hwang, S. and Kim, D., "BERT-based Classification Model for Korean Documents," The Journal of Society for e-Business Studies, Vol. 25, No. 1, 2020.
- Jeon, W., Lee, Y., and Geum, Y., "Airline Service Quality Evaluation Based on Customer Review Using Machine Learning Approach and Sentiment Analysis," The Journal of Society for e-Business Studies, Vol. 26, No. 4, pp. 15-36, 2021.
- Jin, Z., Cao, J., Guo, H., Zhang, Y., and Luo, J., "Multimodal fusion with recurrent neural networks for rumor detection on microblogs," Proceedings of the 25th ACM international conference on Multimedia, 2017.
- Kingma, D. P. and Ba, J., "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
- Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., and Anastasiu, D. C., "Stock price prediction using news sentiment analysis," 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE, 2019.
- Oh, P. and Hwang, B., "Real-time Spatial Recommendation System based on Sentiment Analysis of Twitter," The Journal of Society for e-Business Studies, Vol. 21, No. 3, pp. 15-28, 2016.
- Pannala, N. U., Nawarathna, C. P., Jayakody, J. T. K., Rupasinghe, L., and Krishnadeva, K., "Supervised learning based approach to aspect based sentiment analysis," 2016 IEEE International Conference on Computer and Information Technology (CIT), IEEE, 2016.
- Pires, T., Schlinger, E., and Garrette, D., "How Multilingual is Multilingual BERT?," Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4996-5001, 2019.
- Poria, S., Cambria, E., and Gelbukh, A., "Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis," Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.
- Severyn, A. and Moschitti, A., "Twitter sentiment analysis with deep convolutional neural networks," Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015.
- Smeureanu, I. and Zurini, M., "Spam Filtering for Optimization in Internet Promotions using Bayesian Analysis," Journal of Applied Quantitative Methods, Vol. 5, No. 2, pp. 198-211, 2010.
- Sun, C., Huang, L., and Qiu, X., "Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence," arXiv preprint arXiv:1903.09588, 2019.
- Sutskever, I., Vinyals, O., and Le, Q. V., "Sequence to sequence learning with neural networks," Advances in Neural Information Processing Systems, 2014.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I., "Attention is all you need," In Advances in Neural Information Processing Systems, pp. 6000-6010, 2017.
- Wang, H., Can, D., Kazemzadeh, A., Bar, F., and Narayanan, S., "A system for real- time twitter sentiment analysis of 2012 us presidential election cycle," Proceedings of the ACL 2012 System Demonstrations, 2012.
- Xu, N. and Mao, W., "Multisentinet: A deep semantic network for multimodal sentiment analysis," Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017.
- Yang, L., Li, Y., Wang, J., and Sherratt, R. S., "Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning," IEEE Access, Vol. 8, pp. 23522-23530, 2020. https://doi.org/10.1109/access.2020.2969854