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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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)
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)
Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, New York (2012)
Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 32–38 (2006)
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
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)
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)
Haas, D., Ansel, J., Gu, L., Marcus, A.: Argonaut: macrotask crowdsourcing for complex data processing. Proc. VLDB Endowment 8(12), 1642–1653 (2015)
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)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint (2014). arXiv:1408.5882
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
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)
Liao, S.H.: Expert system methodologies and applicationsa decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
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)
Madden, M., Lenhart, A., Cortesi, S., Gasser, U.: Pew Internet and American Life Project. Pew Research Center, Washington, DC (2010)
Martineau, J., Finin, T.: Delta TFIDF: an improved feature space for sentiment analysis. ICWSM 9, 106 (2009)
Mason, W., Watts, D.J.: Financial incentives and the performance of crowds. ACM SigKDD Explor. Newsl. 11(2), 100–108 (2010)
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)
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)
Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count. In: LIWC 2001, vol. 71. Lawrence Erlbaum Associates, Mahway (2001)
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)
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)
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)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
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)
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)
Yin, M., Chen, Y., Sun, Y.A.: The effects of performance-contingent financial incentives in online labor markets (2013)
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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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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