Skip to main content
SpringerLink
Log in

Sentiment Target Extraction Based on CRFs with Multi-features for Chinese Microblog

  • Conference paper
  • First Online:
Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

Included in the following conference series:

Abstract

Sentiment target extraction on Chinese microblog has attracted increasing research attention. Most previous work relies on syntax, such as automatic parse trees, which are subject to noise for informal text such as microblog. In this paper, we propose a modified CRFs model for Chinese microblog sentiment target extraction. This model see the sentiment target extraction as a sequence-labeling problem, incorporating the contextual information, syntactic rules and opinion lexicon into the model with multi-features. The major contribution of this method is that it can be applied to the texts in which the targets are not mentioned in the sequence. Experimental results on benchmark datasets show that our method can consistently outperform the state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Now Publishers Inc., Hanover (2008)

    Google Scholar 

  2. Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, Berlin (2012)

    MATH  Google Scholar 

  3. Cambria, E., Fu, J., Bisio, F., Poria, S.: Enabling affective intuition for concept-level sentiment analysis. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 508–514 (2015)

    Google Scholar 

  4. Breck, E., Choi, Y., Cardie, C.: Identifying expressions of opinion in context. In: Proceedings of IJCAI 2007, pp. 2683–2688 (2007)

    Google Scholar 

  5. Ren, Y., Zhang, Y., Zhang, M., Ji, D.: Context-sensitive Twitter sentiment extraction using neural network. AAAI-Association for the Advancement of Artificial Intelligence (2016)

    Google Scholar 

  6. Wu, F., Huang, Y.: Personalized microblog sentiment extraction via multi-task learning. In: AAAI (2016)

    Google Scholar 

  7. Xia, R., Wang, C., Dai, X., Li, T.: Co-training for semi-supervised sentiment extraction based on dual-view bags-of-words representation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1054–1063 (2015)

    Google Scholar 

  8. Zhou, G., He, T., Zhao, J., Wu, W.: A subspace learning framework for cross-lingual sentiment extraction with partial parallel data. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), pp. 1426–1432 (2015)

    Google Scholar 

  9. Nakagawa, T., Inui, K., Karohashi, S.: Dependency tree based sentiment extraction using CRFs with hidden variables. In: Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 786–794. Association for Computational Linguistics (2010)

    Google Scholar 

  10. Wu, F., Song, Y., Huang, Y.: Microblog sentiment extraction with contextual knowledge regularization. AAAI-Association for the Advancement of Artificial Intelligence (2015)

    Google Scholar 

  11. Li, G., Hoi, S.C., Chang, K., Jain, R.: Microblogging sentiment detection by collaborative online learning. In: IEEE ICDM, pp. 893–898 (2010)

    Google Scholar 

  12. Song, Y., Lu, Z., Leung, C.W., Yang, Q.: Collaborative boosting for activity classification in microblogs. In: ACM KDD, pp. 482–490 (2013)

    Google Scholar 

  13. Li, G., Hoi, S.C., Chang, K., Liu, W., Jain, R.: Collaborative online multitask learning. IEEE Trans. Knowl. Data Eng. 26(8), 1866–1876 (2014)

    Article  Google Scholar 

  14. Song, K., Feng, S., Gao, W., Wang, D., Yu, G., Wong, K.F.: Personalized sentiment classification based on latent individuality of microblog users. In: IJCAI, pp. 2277–2283 (2015)

    Google Scholar 

  15. Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: WSDM, pp. 537–546 (2013)

    Google Scholar 

  16. Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. (JAIR) 50, 723–762 (2014)

    MATH  Google Scholar 

  17. Wu, F., Song, Y., Huang, Y.: Microblog sentiment classification with contextual knowledge regularization. In: AAAI, pp. 2332–2338 (2015)

    Google Scholar 

  18. Nakagawa, T., Inui, K., Kurohashi, S.: Dependency tree-based sentiment classification using CRFs with hidden variables. In: The 2010 Annual Conference of the North American Chapter of the ACL, pp. 786–794 (2010)

    Google Scholar 

  19. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML 2001, pp. 282–289 (2001)

    Google Scholar 

  20. Sutton, C., McCallum, A.: An introduction to conditional random fields. Mach. Learn. 4(4), 267–373 (2011)

    Article  MATH  Google Scholar 

  21. Nakagawa, T., Inui, K., Kurohashi, S.: Dependency tree-based sentiment classification using CRFs with hidden variables. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 786–794. Association for Computational Linguistics (2010)

    Google Scholar 

  22. Morency, L.P., Quattoni, A., Darrell, T.: Latent-dynamic discriminative models for continuous gesture recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

Download references

Acknowledgments

This work is financially supported by NSFC-Guangdong Joint Found (U1501254), Natural Science Foundation of China (61202269, 61472089, 61572143, 61502108, 61502109), Natural Science Foundation of Guangdong province (2014A030306004, 2014A030308008), Key Technology Research and Development Programs of Guangdong Province (2012B01010029, 2013B051000076, 2015B010108006, 2015B010131015), Science and Technology Plan Project of Guangzhou City (2014Y2-00027), Opening Project of the State Key Laboratory for Novel Software Technology (KFKT2014B03, KFKT2014B23), Philosophy and social science project of Guangdong Provenience (GD14XYJ24), Young innovative talents project of Guangdong Province (2015KQNCX027), The experimental teaching reform project of Guangdong university of technology (2015SY45).

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Guangdong University of Technology, Guangzhou, China

    Bingfeng Chen, Zhifeng Hao, Ruichu Cai, Wen Wen & Shenzhi Du

  2. School of Mathematics and Big Data, Foshan University, Foshan, China

    Zhifeng Hao

Authors
  1. Bingfeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhifeng Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruichu Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shenzhi Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bingfeng Chen .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Chen, B., Hao, Z., Cai, R., Wen, W., Du, S. (2016). Sentiment Target Extraction Based on CRFs with Multi-features for Chinese Microblog. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45835-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation