Abstract
With the rapid growth of Weibo, sentiment analysis on the hot topics which are spotlighted suddenly, spread rapidly, and influence widely during a short period becomes crucial. However, because of the urgent analysis requirement and diversity of the hot topics, the state-of-the-art supervised methods would fail due to the lack of annotated training data. To address this problem, we first propose a Chinese bag-of-opinions model based on dependency grammar representing Weibo sentences. Then, we calculate sentiment polarity score for every opinion and get a weighted summation sentiment evaluation for each sentence. A confidence value of a sentence’s polarity score is also defined. With it, we can extract sentences with high confidences as annotated data which can guide further analysis. We applied our model with the summation evaluation and semi-supervised methods. Experiments conducted on the NLP&CC 2012 dataset for Chinese sentiment analysis validate the effectiveness of our method.
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References
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 36–44. COLING ’10, ACL, Stroudsburg, PA (2010)
Bermingham, A., Smeaton, A.F.: On using twitter to monitor political sentiment and predict election results. In: Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP), IJCNLP 2011, pp. 2–10, 2011
Che, W., Li, Z., Liu, T.: Ltp: a chinese language technology platform. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 13–16. COLING ’10, ACL, Stroudsburg, PA (2010)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 241–249. COLING ’10, ACL, Stroudsburg, PA (2010)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing 150(12), 1–6 (2009)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177. KDD ’04, ACM, New York, NY (2004)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - vol. 1, pp. 151–160. ACL, Stroudsburg, PA (2011)
Joshi, A., Balamurali, A.R., Bhattacharyya, P., Mohanty, R.: C-feel-it: a sentiment analyzer for micro-blogs. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations, pp. 127–132. HLT ’11, ACL, Stroudsburg, PA (2011)
Liu, J., Seneff, S.: Review sentiment scoring via a parse-and-paraphrase paradigm. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: vol. 1 - Volume 1, pp. 161–169. EMNLP ’09, ACL, Stroudsburg, PA (2009)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - vol. 10, pp. 79–86. EMNLP ’02, ACL, Stroudsburg, PA (2002)
Qu, L., Ifrim, G., Weikum, G.: The bag-of-opinions method for review rating prediction from sparse text patterns. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 913–921. ACL, Stroudsburg, PA (2010)
Tesniére, L.: Eléments de Syntaxe Structurale. Klincksiek, Paris, FRA (1959)
Tumasjan, A., Sprenger, T., Sandner, P., Welpe, I.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185 (2010)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. ACL ’02, ACL, Stroudsburg, PA (2002)
Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1031–1040. CIKM ’11, ACM, New York, NY (2011)
Whitelaw, C., Garg, N., Argamon, S.: Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 625–631. CIKM ’05, ACM, New York (2005)
Zagibalov, T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of chinese text. In: Proceedings of the 22nd International Conference on Computational Linguistics - vol. 1, pp. 1073–1080. COLING ’08, ACL, Stroudsburg, PA (2008)
Acknowledgements
This work is funded by the National Program on Key Basic Research Project(973 Program, Grant No.2013CB329605), Natural Science Foundation of China (NSFC, Grant Nos. 60873237 and 61003168), Natural Science Foundation of Beijing (Grant No.4092037), Outstanding Young Teacher Foundation, and Basic Research Foundation of Beijing Institute of Technology and partially supported by Beijing Key Discipline Program.
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Wang, J., Song, D., Liao, L., Zou, W., Yan, X., Su, Y. (2013). The Chinese Bag-of-Opinions Method for Hot-Topic-Oriented Sentiment Analysis on Weibo. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, HT. (eds) Semantic Web and Web Science. Springer Proceedings in Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6880-6_31
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DOI: https://doi.org/10.1007/978-1-4614-6880-6_31
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