Skip to main content

Aspect Based Sentiment Analysis for Online Reviews

  • Conference paper
  • First Online:
Advances in Computer Science and Ubiquitous Computing (CUTE 2017, CSA 2017)

Abstract

Learning good semantic vector representations for sentiment analysis in phrases, sentences and paragraphs is a challenging and ongoing area of natural language processing. In this paper, we propose a Convolution Neural Network for aspect level sentiment classification. Our model first builds a convolution neural network model to aspect extraction. Afterwards, we used a sequence labeling approach with Conditional Random Fields for the opinion target detection. Finally, we concatenate an aspect vector with every word embedding and apply a convolution neural network over it to determine the sentiment towards an aspect. Results of an experiment show that our method performs comparably well on Yelp reviews.

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. Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, San Rafael (2012)

    Google Scholar 

  2. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177. ACM, New York (2004)

    Google Scholar 

  3. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retriev. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  4. Nguyen, T.H., Shirai, K.: PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the 2015 Conference on Empirical Method Shirai (2015)

    Google Scholar 

  5. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1746–1751. http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf.

  6. Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, pp. 1–12. Erlbaum, Hillsdale, NJ (1986)

    Google Scholar 

  7. Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of the Advances in Neural Information Processing Systems, NIPS 2013, vol. 26, pp. 1–10 (2013)

    Google Scholar 

  8. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 655–665 (2014)

    Google Scholar 

  9. Tang, D., Qin, B., Feng, X., Liu, T.: Target-Dependent Sentiment Classification with Long Short Term Memory. arXiv preprint arXiv:1512.01100 (2015)

  10. Machine Learning for Language Toolkit

    Google Scholar 

  11. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed Representations of Words and Phrases and their Compositionality (2013)

    Google Scholar 

  12. Socher, R., Perelygin, A., Wu, J.Y., Chuang, J.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP (2013)

    Google Scholar 

  13. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing coadaptation of feature detectors. arXiv: 1207.0580, pp. 1–18 (2012). Nair and Hinton, 2010. http://doi.org/arXiv:1207.0580

  14. Kudo, T.: CRF++: Yet another CRF toolkit. Software (2005). http://crfpp.courceforge.net

  15. http://www.yelp.com/dataset_challenge

  16. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224 N Project Report, Stanford, pp. 1–12 (2009)

    Google Scholar 

  17. Zhang, Min-ling, Zhou, Zhi-hua, Member, Senior: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chunyong Yin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, L., Liu, J., Wang, L., Yin, C. (2018). Aspect Based Sentiment Analysis for Online Reviews. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_78

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7605-3_78

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7604-6

  • Online ISBN: 978-981-10-7605-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics