Skip to main content

Advertisement

Springer Nature Link
Log in

Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures

  • Published:
Applied Intelligence Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics, making them ideal platforms on which to conduct electronic opinion polls on products, services and public figures. However, conventional sentiment analysis methods for microblogging messages may not meet the demands of opinion polls for public figures. Therefore, in this study, we focus mainly on the problem of sentiment analysis for opinion polling on Chinese public figures. We propose a sentiment parsing-based architecture, which represents and labels opinion targets and their corresponding sentiments jointly to avoid the mismatching of them, for opinion poll of public figures using microblogs. Furthermore, we formulate sentiment parsing of microblogging sentences as a sequence labeling problem and adapt different Recurrent Neural Network (RNN) models to train and infer the model. Our experimental results demonstrate that the proposed sentiment parsing-based methods achieve better performance than conventional sentiment score-based methods in opinion polling on public figures using microblogs.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://ictclas.nlpir.org/downloads.

  2. https://github.com/fchollet/keras.

  3. http://radimrehurek.com/gensim/.

References

  1. Jansen BJ, Zhang M, Sobel K, Chowdury A (2009) Micro-blogging as online word of mouth branding. In: CHI’09 Extended abstracts on human factors in computing systems. ACM, pp 3859–3864

  2. Jansen BJ, Zhang M, Sobel K, Chowdury A (2009) Twitter power: tweets as electronic word of mouth. J Am Soc Inf Sci Technol 60(11):2169–2188

    Article  Google Scholar 

  3. O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11(122–129):1–2

    Google Scholar 

  4. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment. ICWSM 10:178–185

    Google Scholar 

  5. Chung JE, Mustafaraj E (2011) Can collective sentiment expressed on twitter predict political elections?. In: AAAI

  6. Sang ETK, Bos J (2012) Predicting the 2011 dutch senate election results with twitter. In: Proceedings of the workshop on semantic analysis in social media. Association for Computational Linguistics, pp 53–60

  7. Contractor D, Chawda B, Mehta S, Subramaniam LV, Faruquie TA (2015) Tracking political elections on social media: applications and experience. In: Proceedings of the 24th International conference on artificial intelligence. AAAI Press, pp 2320–2326

  8. Pascanu R, Gulcehre C, Cho K, Bengio Y (2013) How to construct deep recurrent neural networks. arXiv:1312.6026

  9. Graves A, Mohamed A-r, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 6645–6649

  10. Doetsch P, Kozielski M, Ney H (2014) Fast and robust training of recurrent neural networks for offline handwriting recognition. In: 2014 14th International conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 279–284

  11. Mikolov T (2012) Statistical language models based on neural networks. Thesis, Brno University of Technology

  12. Yazdani M, Henderson J (2015) Incremental recurrent neural network dependency parser with search-based discriminative training. In: Proceedings of the 19th Conference on Computational Language Learning, pp 142–152

  13. Metaxas PT, Mustafaraj E, Gayo-Avello D (2011) How (not) to predict elections. In: 2011 IEEE third international conference on privacy, security, risk and trust (PASSAT) and 2011 IEEE third international conference on social computing (SocialCom). IEEE, pp 165–171

  14. Kagan V, Stevens A, Subrahmanian VS (2015) Using twitter sentiment to forecast the 2013 pakistani election and the 2014 indian election. IEEE Intell Syst 30(1):2–5

    Article  Google Scholar 

  15. Luong TTB, Houston D (2015) Public opinions of light rail service in los angeles, an analysis using twitter data. iConference 2015 Proceedings

  16. Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5 (1):1–167

    Article  Google Scholar 

  17. Zhang C (2009) Sentiment analysis of chinese documents: from sentence to document level. J Am Soc Inf Sci Technol

  18. Bollegala D, Weir D, Carroll J (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans Knowl Data Eng 25(8):1719–1731

    Article  Google Scholar 

  19. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10. Association for Computational Linguistics, pp 79–86

  20. Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: EMNLP, vol 4, pp 412–418

  21. Socher R, Pennington J, Huang EH, Ng AY, Manning CD (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 151–161

  22. Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), vol 1631. Citeseer, p 1642

  23. Bravo-Marquez F, Mendoza M, Poblete B (2013) Combining strengths, emotions and polarities for boosting twitter sentiment analysis. In: Proceedings of the second international workshop on issues of sentiment discovery and opinion mining. ACM, p 2

  24. Hu X, Tang J, Gao H, Liu H (2013) Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd international conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp 607–618

  25. Yi J, Nasukawa T, Bunescu R, Niblack W (2003) Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Third IEEE international conference on data mining, 2003. ICDM 2003. IEEE, pp 427–434

  26. Popescu A-M, Etzioni O (2007) Extracting product features and opinions from reviews. In: Natural language processing and text mining. Springer, pp 9–28

  27. Li F, Han C, Huang M, Zhu X, Xia Y-J, Zhang S, Hao Y (2010) Structure-aware review mining and summarization. In: Proceedings of the 23rd international conference on computational linguistics. Association for Computational Linguistics, pp 653–661

  28. Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 720–728

  29. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  30. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li F-F (2014) Large-scale video classification with convolutional neural networks. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1725–1732

  31. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

    MATH  Google Scholar 

  32. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. arXiv:1405.4053

  33. d’Avila Garcez AS, Broda K, Gabbay DM (2001) Symbolic knowledge extraction from trained neural networks: a sound approach. Artif Intell 125(1):155–207

    Article  MathSciNet  MATH  Google Scholar 

  34. Rosaci D (2007) Cilios: connectionist inductive learning and inter-ontology similarities for recommending information agents. Inf Syst 32(6):793–825

    Article  Google Scholar 

  35. Chen D, Socher R, Manning CD, Ng AY (2013) Learning new facts from knowledge bases with neural tensor networks and semantic word vectors. arXiv:1301.3618

  36. d’Avila Garcez AS, Gabbay DM, Ray O, Woods J (2007) Abductive reasoning in neural-symbolic systems. Topoi 26(1):37–49

    Article  MathSciNet  MATH  Google Scholar 

  37. Socher R, Chen D, Manning CD, Ng A (2013) Reasoning with neural tensor networks for knowledge base completion. In: Advances in neural information processing systems, pp 926– 934

  38. Mesnil G, He X, Li D, Bengio Y (2013) Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding, pp 3771–3775

  39. Mesnil G, Dauphin Y, Yao K, Bengio Y, Deng L, Hakkani-Tur D, He X, Heck L, Tur G, Yu D (2015) Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Trans Audio Speech Lang Process 23(3):530– 539

    Article  Google Scholar 

  40. Liwicki M, Graves A, Bunke H (2012) Neural networks for handwriting recognition. In: Computational intelligence paradigms in advanced pattern classification. Springer, pp 5–24

  41. Legrand J, Collobert R (2014) Joint rnn-based greedy parsing and word composition. arXiv:1412.7028

  42. Li L, Jin L, Jiang Z, Song D, Huang D (2015) Biomedical named entity recognition based on extended recurrent neural networks. In: 2015 IEEE International Conference on bioinformatics and biomedicine (BIBM), pp 649–652

  43. Chiu JPC, Nichols E (2015) Named entity recognition with bidirectional lstm-cnns. arXiv:1511.08308

  44. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  45. Graves A (2012) Supervised sequence labelling with recurrent neural networks, vol 385. Springer

  46. Cho K, Van Merrinboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078

  47. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555

  48. Jozefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: Proceedings of the 32nd international conference on machine learning (ICML), pp 2342–2350

  49. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45 (11):2673–2681

    Article  Google Scholar 

  50. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  51. Kingma D, Adam JB (2014) A method for stochastic optimization. arXiv:1412.6980

  52. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. arXiv:1405.4053

  53. Lai S, Liu K, Xu L, Zhao J (2015) How to generate a good word embedding? arXiv:1507.05523

  54. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. arXiv:1310.4546

Download references

Acknowledgments

The research is supported by National Natural Science Foundation of China (No.71331008).

Author information

Authors and Affiliations

  1. College of Information Systems and Management, National University of Defense Technology, Changsha, People’s Republic of China

    Jiajun Cheng, Xin Zhang, Pei Li, Sheng Zhang, Zhaoyun Ding & Hui Wang

Authors
  1. Jiajun Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Pei Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Sheng Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zhaoyun Ding
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Hui Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jiajun Cheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, J., Zhang, X., Li, P. et al. Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures. Appl Intell 45, 429–442 (2016). https://doi.org/10.1007/s10489-016-0768-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0768-0

Keywords