References
[1]. Pang, B., Lee, L., & Vaithyanathan, S. (2002). “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts.” Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), 271-278.
[2]. Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). “Learning Word Vectors for Sentiment Analysis.” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 142-150.
[3]. Systems (NeurIPS)Bollen, J., Mao, H., & Zeng, X. (2011). “Twitter Mood Predicts the Stock Market.” Journal of Computational Science, 2(1), 1-8.
[4]. Oghina, Andrei, Ciprian Breazu, Bogdan Butoi, and Teodor Stratulat. “Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums.
[5]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” arXiv preprint arXiv:1810.04805.
[6]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems (NeurIPS).
[7]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[8]. Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing machines. arXiv preprint arXiv:1410.5401.
[9]. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078
[10]. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555
Cite this article
Gan,Z.;Zhu,Z. (2024). An empirical study on how emotion affects the probability of replies based BERT. Applied and Computational Engineering,41,148-152.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Pang, B., Lee, L., & Vaithyanathan, S. (2002). “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts.” Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), 271-278.
[2]. Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). “Learning Word Vectors for Sentiment Analysis.” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 142-150.
[3]. Systems (NeurIPS)Bollen, J., Mao, H., & Zeng, X. (2011). “Twitter Mood Predicts the Stock Market.” Journal of Computational Science, 2(1), 1-8.
[4]. Oghina, Andrei, Ciprian Breazu, Bogdan Butoi, and Teodor Stratulat. “Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums.
[5]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” arXiv preprint arXiv:1810.04805.
[6]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems (NeurIPS).
[7]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[8]. Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing machines. arXiv preprint arXiv:1410.5401.
[9]. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078
[10]. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555