Abstract
In the information age, the network technology continues to develop. As an emerging social media, Sina Weibo has a huge user base. Every day, hundreds of millions of users express their opinions on hot events, or share the joys and worries in life on the Weibo platform. Therefore, the analysis of the user’s emotion has broad application prospects, which could also be used in the fields of public opinion monitoring, opinion guidance, and advertisement placement. This paper proposes a microblog sentiment classification method based on dual attention mechanism and bidirectional LSTM. Firstly, the bidirectional LSTM model is used to semantically encode the microblog text, then the self-attention and sentiment word attention are introduced into the bidirectional LSTM model. Finally, the Softmax classifier is used to classify the sentiment of microblog. In order to verify the validity of the model, several groups of comparative experiments are carried out, which use NLPCC2013 and NLPCC2014 evaluation task datasets as experimental data sets. The results show that the proposed microblog sentiment classification model based on dual attention mechanism and bidirectional LSTM is superior.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhu, Y.L., Min, J., Zhou, Y.Q., et al.: Semantic orientation computing based on HowNet. J. Chin. Inf. Process. 20(1), 16–22 (2006). https://doi.org/10.3969/j.issn.1003-0077.2006.01.003. (in Chinese)
Hou, M., Teng, Y.L., Li, X.Y., et al.: Study on the linguistic features of the topic-oriented microblog and the strategies for its sentiment analysis. J. Appl. Linguist. 86(2), 135–143 (2013). https://doi.org/10.16499/j.cnki.1003-5397.2013.02.019. (in Chinese)
Wu, Q.L., Wang, Y.: Research on the emotional feature selection method in the Chinese microblog. J. Inner Mongolia Normal Univ. 45(1), 84–88 (2016). https://doi.org/10.3969/j.issn.1001-8735.2016.01.020. (in Chinese)
Hatzivassiloglou, V., Mckeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the ACL, pp. 174–181 (1997). https://doi.org/10.3115/979617.979640
Song, S.Y., Li, Q.D., Lu, D.Y.: Hot event sentiment analysis method in micro-blogging. J. Comput. Sci. 39(Z6), 226–228 (2012). (in Chinese)
He, Y., Deng, W.R., Zhang, D.: Study on sentiments recognition and classification of Chinese micro-blog. J. Intell. 2, 136–139 (2014). https://doi.org/10.3969/j.issn.1002-1965.2014.02.026. (in Chinese)
Pang, L., Li, S.S., Zhou, G.D.: Sentiment classification method of Chinese micro-blog based on emotional knowledge. J. Comput. Eng. 38(13), 156–168,162 (2012). https://doi.org/10.3969/j.issn.1000-3428.2012.13.046. (in Chinese)
Zhang, S., Yu, L.B., Hu, C.J.: Sentiment analysis of Chinese micro-blogs based on emoticons and emotional words. J. Comput. Sci. 39(Z11), 146–148 (2012). https://doi.org/10.3969/j.issn.1002-137x.2012.z3.041. (in Chinese)
Liu, Z.M., Liu, L.: Empirical study of sentiment classification for Chinese microblog based on machine learning. J. Comput. Eng. Appl. 48(1), 1–4 (2012). https://doi.org/10.3778/j.issn.1002-8331.2012.01.001. (in Chinese)
Li, T.T., Ji, D.H.: Sentiment analysis of micro-blog based on SVM and CRF using various combinations of features. J. Appl. Res. Comput. 32(4), 978–981 (2015). https://doi.org/10.3969/j.issn.1001-3695.2015.04.004. (in Chinese)
Hinton, G.E., Salakhutdinov, P.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Yang, X., Macdonald, C., Ounis, I.: Using word embeddings in Twitter election classification. Inf. Retrieval J. 21(2–3), 183–207 (2018). https://doi.org/10.1007/s10791-017-9319-5
Socher, R., Perelygin, A., Wu, J., 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, pp. 1631–1642 (2013)
Sun, S.T., He, Y.X.: Multi-label emotion classification for microblog based or CNN feature space. J. Adv. Eng. Sci. 49(3), 162–169 (2017). https://doi.org/10.15961/j.jsuese.201600780. (in Chinese)
He, Y.X., Sun, S.T., Niu, F.F., et al.: A deep learning model enhanced with emotion semantics for microblog sentiment analysis. Chin. J. Comput. 40(4), 773–790 (2017). https://doi.org/10.11897/sp.j.1016.2017.00773. (in Chinese)
Meng, W., Wei, Y.Q., Liu, W.F.: Target-specific sentiment analysis based on CRT mechanism hybrid neural network. J. Appl. Res. Comput. (2019). https://doi.org/10.19734/j.issn.1001-3695.2018.08.0538. (in Chinese)
Vaswani, A., Shazeer, N., Parmar, N.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Xu, L.H., Li, H.F., Pan, Y., et al.: Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 27(2), 180–185 (2008). https://doi.org/10.3969/j.issn.1000-0135.2008.02.004. (in Chinese)
Wawre, S.V., Deshmukh, S.N.: Sentiment classification using machine learning techniques. Int. J. Sci. Res. 5(4), 819–821 (2016)
Jiang, F., Liu, Y.Q., Luan, H.B., et al.: Microblog sentiment analysis with emoticon space model. J. Comput. Sci. Technol. 30(5), 1120–1129 (2015). https://doi.org/10.1007/s11390-015-1587-1
Acknowledgements
This work was supported by grants from National Nature Science Foundation of China (No. 61772081).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, W., Zhang, Y., Duan, R., Zhang, W. (2020). Microblog Sentiment Classification Method Based on Dual Attention Mechanism and Bidirectional LSTM. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-38189-9_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38188-2
Online ISBN: 978-3-030-38189-9
eBook Packages: Computer ScienceComputer Science (R0)