Abstract
The sentimental analysis of Chinese microblog is a crucial task for social network related applications, such as internet marketing, public opinion monitoring, etc. This paper proposes a two-phase computing model for microblog sentimental analysis, including topic classification for posts and topic sentimental analysis respectively. In the first phase, the Latent Dirichlet Allocation(LDA) model is employed into our model for topic classification. The topics of posts are scattered into the microblogs, it has some uncertainty. The LDA model can classify the fuzzy topics. In the second phase, sentimental dictionary and emotion knowledge are performed into our model for topic sentimental analysis. HowNet as the sentimental dictionary is used to the sentimental tendency analysis. The emotion knowledge mainly uses symbols in microblog. Besides of sentimental knowledge, the sliding window for sentimental analysis also introduced into the model as the modification at the sentence level. The experimental results show that this method achieves good performance.
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
References
Dong, L., Weiy, F., Liuy, S., Zhouy, M., Xu, K.: A statistical parsing framework for sentiment classification. In: ACL, pp. 265–308 (2015)
Feng, S., Kang, J.S., Kuznetsova, P., Choi, Y.: Connotation lexicon: a dash of sentiment beneath the surface meaning. In: ACL, pp. 1774–1784 (2015)
Jun-xia, C., Zhi-tang, L., Ming-guang, Z., Jin, X.: Novel topic detection method for microblog based on svm filtration. J. Commun. 34(z2), 74–78 (2013)
Lazaridou, A., Titov, I.: A bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. In: ACL, pp. 1630–1639 (2015)
Lipenkova, J.: A system for fine-grained aspect-based sentiment analysis of Chinese. In: ACL, pp. 55–60 (2015)
Mohtarami, M., Lan, M., Tan, C.L.: Probabilistic sense sentiment similarity through hidden emotions. In: ACL, pp. 983–902 (2013)
Ramesh, A., Kumar, S.H., Foulds, J., Getoor, L.: Weakly supervised models of aspectsentiment for online course discussion forums. In: ACL. pp. 74–83 (2015)
Wang, L., Liu, K., Cao, Z., Zhao, J., de Melo, G.: Sentiment-aspect extraction based on restricted boltzmann machines. In: ACL, pp. 616–625 (2015)
Wang, Z., Lee, S.Y.M., Li, S., Zhou, G.: Emotion detection in code-switching texts via bilingual and sentimental information. In: ACL, pp. 763–768 (2015)
Xu, L., Liu, K., Lai, S., Chen, Y., Zhao, J.: Mining opinion words and opinion targets in a two-stage framework. In: ACL, pp. 1764–1773 (2013)
Acknowledgment
The authors are grateful to the reviewers for reviewing this paper. This work is supported by the National Science Foundation of China (Grant No.61103112), Social Science Foundation of Beijing(Grant No.13SHC031), Ministry of Education of China (Project of Humanities and Social Sciences, Grant NO.13YJC740055) and Beijing Young talent plan(Grant No.CIT&TCD201404005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Duan, J., Wang, C., Zhang, M., Liu, H. (2016). Two-Phase Computing Model for Chinese Microblog Sentimental Analysis. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-40973-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40972-6
Online ISBN: 978-3-319-40973-3
eBook Packages: Computer ScienceComputer Science (R0)