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Converging Human Knowledge for Opinion Mining

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Book cover Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 612))

Abstract

Opinion mining focuses on analyzing opinions in documents. Existing most algorithms for mining opinion either are machine-only, leaving plenty of confused puzzles due to lacking human background knowledge, or using opinion dictionary from domain experts. The latter is expensive and hard to scale. In this paper, we propose a novel approach RULING (conveRging hUman knowLedge opInion miNinG) for opinion mining, where human include both the crowd and the experts. Firstly, we propose a method for combining expert knowledge with the machine learning method. Then we use the prediction result to find out the hard item, and classify them using crowdsourcing. This method can scale better than the previous methods and get a better result. Experimental results demonstrate our RULING approach outperforms related proposals in terms of classification performance.

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References

  1. Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. (TOIS) 26(3), 12 (2008)

    Article  Google Scholar 

  2. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, Association for Computational Linguistics, pp. 30–38 (2011)

    Google Scholar 

  3. Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, New York (2012)

    Book  Google Scholar 

  4. Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 32–38 (2006)

    Google Scholar 

  5. Boia, M., Musat, C.C., Faltings, B.: Acquiring commonsense knowledge for sentiment analysis using human computation. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014). doi:10.1145/2567948.2577316

  6. Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 6(1), 3–5 (2011)

    Article  Google Scholar 

  7. Feyisetan, O., Simperl, E., Van Kleek, M., Shadbolt, N.: Improving paid microtasks through gamification and adaptive furtherance incentives. In: Proceedings of the 24th International Conference on World Wide Web. ACM (2015)

    Google Scholar 

  8. Haas, D., Ansel, J., Gu, L., Marcus, A.: Argonaut: macrotask crowdsourcing for complex data processing. Proc. VLDB Endowment 8(12), 1642–1653 (2015)

    Article  Google Scholar 

  9. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2004)

    Google Scholar 

  10. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint (2014). arXiv:1408.5882

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)

    Google Scholar 

  12. Law, E., Yin, M., Goh, J., Chen, K., Terry, M.A., Gajos, K.Z.: Curiosity killed the cat, but makes crowdwork better. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM (2016)

    Google Scholar 

  13. Liao, S.H.: Expert system methodologies and applicationsa decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)

    Article  Google Scholar 

  14. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  MathSciNet  Google Scholar 

  15. Liu, X., Lu, M., Ooi, B.C., Shen, Y., Wu, S., Zhang, M.: CDAS: a crowdsourcing data analytics system. Proc. VLDB Endowment 5(10), 1040–1051 (2012)

    Article  Google Scholar 

  16. Madden, M., Lenhart, A., Cortesi, S., Gasser, U.: Pew Internet and American Life Project. Pew Research Center, Washington, DC (2010)

    Google Scholar 

  17. Martineau, J., Finin, T.: Delta TFIDF: an improved feature space for sentiment analysis. ICWSM 9, 106 (2009)

    Google Scholar 

  18. Mason, W., Watts, D.J.: Financial incentives and the performance of crowds. ACM SigKDD Explor. Newsl. 11(2), 100–108 (2010)

    Article  Google Scholar 

  19. McAuley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2015)

    Google Scholar 

  20. Mozafari, B., Sarkar, P., Franklin, M., Jordan, M., Madden, S.: Scaling up crowd-sourcing to very large datasets: a case for active learning. Proc. VLDB Endowment 8(2), 125–136 (2014)

    Article  Google Scholar 

  21. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count. In: LIWC 2001, vol. 71. Lawrence Erlbaum Associates, Mahway (2001)

    Google Scholar 

  22. Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)

    Google Scholar 

  23. Shaw, A.D., Horton, J.J., Chen, D.L.: Designing incentives for inexpert human raters. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work. ACM (2011)

    Google Scholar 

  24. Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, vol. 1631, p. 1642. Citeseer (2013)

    Google Scholar 

  25. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  26. Wilson, S., Schaub, F., Ramanath, R., Sadeh, N., Liu, F., Smith, N.A., Liu, F.: Crowdsourcing annotations for websites’ privacy policies: can it really work? In: Proceedings of the 25th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee (2016)

    Google Scholar 

  27. Wu, H., Sun, H., Fang, Y., Hu, K., Xie, Y., Song, Y., Liu, X.: Combining machine learning and crowdsourcing for better understanding commodity reviews. In: AAAI (2015)

    Google Scholar 

  28. Yin, M., Chen, Y., Sun, Y.A.: The effects of performance-contingent financial incentives in online labor markets (2013)

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China projects under Grants 91438121, 61373156, 61672351, 61532013 and U1636210, in part by the National Basic Research Program under Grant 2015CB352403, in part by the Huawei Technologies Co. Ltd., project under Grant YBN2016090103, and in part by the National Key Research and Development Program of China under Grant 2016YFB0700502 and the Scientific Innovation Act of STCSM under Grant 15JC1402400.

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Authors and Affiliations

  1. School of Software, Shanghai Jiaotong University, No. 800, Dongchuan Road, Minghang, Shanghai, 200240, China

    Jiacheng Liu, Long Chen & Liang Qiao

  2. Department of Computer Science and Engineering, Shanghai Jiaotong University, No. 800, Dongchuan Road, Minghang, Shanghai, 200240, China

    Feilong Tang

  3. Department of Computer Science and Technology, East China Normal University, Shanghai, China

    Yanqin Yang & Wenchao Xu

  4. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China

    Yanqin Yang & Wenchao Xu

Authors
  1. Jiacheng Liu
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  2. Feilong Tang
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  3. Long Chen
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  4. Liang Qiao
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  5. Yanqin Yang
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  6. Wenchao Xu
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Corresponding author

Correspondence to Feilong Tang .

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Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Rissho University, Tokyo, Japan

    Tomoya Enokido

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Liu, J., Tang, F., Chen, L., Qiao, L., Yang, Y., Xu, W. (2018). Converging Human Knowledge for Opinion Mining. In: Barolli, L., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2017. Advances in Intelligent Systems and Computing, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-61542-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-61542-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61541-7

  • Online ISBN: 978-3-319-61542-4

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