Abstract
Traditional models for sentiment classification are trained and tested on the same dataset. However, the model parameters trained on one dataset are not suitable for another dataset and it takes much time to train a new model. In this paper, we propose a generic model based on multiple domains for sentiment classification (DCSen). In DCSen, domain classification is used to generalize the sentiment classification model, so the trained model’s parameters can be applied to different datasets in given domains. Specifically, the document is first mapped to the domain distribution which is used as a bridge between domain classification and sentiment classification, and then sentiment classification is completed. In order to make DCSen more generic, the sentiment lexicon is introduced to select the sentences in a document and the more representative datasets are obtained. For the purpose of improving accuracy and reducing training time, transfer learning based on neutral networks is used to get the document embeddings. Extensive experiments on the datasets of 15 different domains show that DCSen can achieve better performance compared with traditional models in the aspect of generality.
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References
Iyyer, M., Manjunatha, V., Boyd-Graber, J., et al.: Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of ACL, vol. 1, pp. 1681–1691 (2015)
Zhou, P., Qi, Z., Zheng, S., et al.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint, arXiv:1611.06639 (2016)
Conneau, A., Kiela, D., Schwenk, H., et al.: Supervised learning of universal sentence representations from natural language inference data. arXiv preprint, arXiv:1705.02364 (2017)
Al-Moslmi, T., Omar, N., Abdullah, S., et al.: Approaches to cross-domain sentiment analysis: a systematic literature review. IEEE Access 5, 16173–16192 (2017)
Ren, Y., Zhang, Y., Zhang, M., et al.: Context-sensitive twitter sentiment classification using neural network. In: AAAI, pp. 215–221 (2016)
Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. arXiv preprint, arXiv:1704.05742 (2017)
Bollegala, D., Weir, D., Carroll, J.: Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans. Knowl. Data Eng. 25(8), 1719–1731 (2013)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint, arXiv:1404.2188 (2014)
dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 69–78 (2014)
Collobert, R., Weston, J., Bottou, L., et al.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)
Lai, S., Xu, L., Liu, K., et al.: Recurrent convolutional neural networks for text classification. In: AAAI, vol. 333, pp. 2267–2273 (2015)
Teng, Z., Vo, D.T., Zhang, Y.: Context-sensitive lexicon features for neural sentiment analysis. In: EMNLP, pp. 1629–1638 (2016)
Bowman, S.R., Angeli, G., Potts, C., et al.: A large annotated corpus for learning natural language inference. arXiv preprint, arXiv:1508.05326 (2015)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(Jul), 2121–2159 (2011)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of ACL, pp. 440–447 (2007)
Maas, A.L., Daly, R.E., Pham, P.T., et al.: Learning word vectors for sentiment analysis. In: Proceeidngs of ACL, pp. 142–150 (2011)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of ACL, pp. 115–124 (2005)
Socher, R., Perelygin, A., Wu, J., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, pp. 1631–1642 (2013)
Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. LREC, 10(2010), 2200–2204 (2010)
Pennington, J., Socher, R., Manning C.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint, arXiv:1308.0850 (2013)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61672108) funded by the China government, Ministry of Science and Technology.
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Qu, Z., Zhao, Y., Wang, X., Wu, C. (2018). A Generic Model Based on Multiple Domains for Sentiment Classification. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_37
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DOI: https://doi.org/10.1007/978-3-319-93803-5_37
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