Skip to main content

Deep Neural Network for Short-Text Sentiment Classification

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9645))

Included in the following conference series:

  • 1824 Accesses

Abstract

As a concise medium to describe events, short text plays an important role to convey the opinions of users. The classification of user emotions based on short text has been a significant topic in social network analysis. Neural Network can obtain good classification performance with high generalization ability. However, conventional neural networks only use a simple back-propagation algorithm to estimate the parameters, which may introduce large instabilities when training deep neural networks by random initializations. In this paper, we apply a pre-training method to deep neural networks based on restricted Boltzmann machines, which aims to gain competitive and stable classification performance of user emotions over short text. Experimental evaluations using real-world datasets validate the effectiveness of our model on the short-text sentiment classification task.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bengio, Y.: Learning deep architectures for ai. Found. Trends\(\textregistered \) Mach. Learn., vol. 2(1), pp. 1–127 (2009)

    Google Scholar 

  2. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Sci. 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. (NIPS) 19, 153 (2007)

    Google Scholar 

  4. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86 (2002)

    Google Scholar 

  5. Katz, P., Singleton, M., Wicentowski, R.: Swat-mp: the semeval- systems for task 5 and task 14. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval), pp. 308–313(2007)

    Google Scholar 

  6. Bao, S., Xu, S., Zhang, L., Yan, R., Su, Z., Han, D., Yu, Y.: Mining social emotions from affective text. IEEE Trans. Knowl. Data Eng. 24(9), 1658–1670 (2012)

    Article  Google Scholar 

  7. Rao, Y., Li, Q., Mao, X., Wenyin, L.: Sentiment topic models for social emotion mining. Inf. Sci. 266, 90–100 (2014)

    Article  Google Scholar 

  8. Rao, Y., Li, Q., Wenyin, L., Wu, Q., Quan, X.: Affective topic model for social emotion detection. Neural Netw. 58, 29–37 (2014)

    Article  Google Scholar 

  9. Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th International Conference on World Wide Web (WWW), pp. 377–386 (2006)

    Google Scholar 

  10. Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 787–788 (2007)

    Google Scholar 

  11. Tesauro, G.: Practical issues in temporal difference learning. Mach. Learn. 8(3–4), 33–53 (1992)

    MATH  Google Scholar 

  12. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  13. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)

    Google Scholar 

  15. Strapparava, C., Mihalcea, R.: Semeval- task 14: Affective text. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval 2007), pp. 70–74 (2007)

    Google Scholar 

  16. Scherer, K.R., Wallbott, H.G.: Evidence for universality and cultural variation of differential emotion response patterning. J. Pers. Soc. Psychol. 66(2), 310 (1994)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (61502545, 61472453, U1401256, U1501252), the Fundamental Research Funds for the Central Universities, and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanghui Rao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Li, X., Pang, J., Mo, B., Rao, Y., Wang, F.L. (2016). Deep Neural Network for Short-Text Sentiment Classification. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32055-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32054-0

  • Online ISBN: 978-3-319-32055-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics