Abstract
Aspect-based sentiment analysis aims at identifying the sentiment polarity of specific target in its context. Researches mainly focus on the ways for exploring the sentiment polarity based on explicit aspect of different products. The existing approaches have realized the sentiment classification with given targets and developed various methods with the goal of precisely polarity classification. However, there are no given explicit aspects in most practical scenarios. In this paper, we propose a pair-wise method which merges aspect-sentiment pair extraction and polarity classification in an unified framework. We convert the aspect-sentiment pairs detection process into a pairs binary classification problem correspondingly. Meanwhile, we construct a feature system applied to opinion mining. The experimental results on CCF BDCI 2017 aspect-based sentiment analysis shared task dataset show that our proposed pair-wise method obtained good performance with a 0.718 F1 score which outperforms most proposed methods.
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Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, 2–4 June 2010, Los Angeles, California, USA, pp. 804–812 (2010)
Che, W., Li, Z., Liu, T.: LTP: A Chinese language technology platform. J. Chin. Inf. Process. 2(6), 13–16 (2010)
Che, W., Zhao, Y., Guo, H., Su, Z., Liu, T.: Sentence compression for aspect-based sentiment analysis. IEEE/ACM Trans. Audio Speech Lang. Process. 23(12), 2111–2124 (2015). https://doi.org/10.1109/TASLP.2015.2443982
Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System, pp. 785–794 (2016)
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Meeting of the Association for Computational Linguistics, pp. 49–54 (2014)
Fellbaum, C., Miller, G.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Hatzivassiloglou, V., Mckeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the ACL, pp. 174–181 (1997)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, August, pp. 168–177 (2004)
Jakob, N., Gurevych, I.: Using anaphora resolution to improve opinion target identification in movie reviews. In: Meeting of the Association for Computational Linguistics (2010)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of Tricks for Efficient Text Classification, pp. 427–431 (2016)
Kim, S., Zhang, J., Chen, Z., Oh, A., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 526–533 (2013)
Kim, Y.: Convolutional Neural Networks for Sentence Classification. Eprint Arxiv (2014)
Lafferty, J.D., Mccallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan Publishers, San Rafael (2012)
Ma, D., Li, S., Zhang, X., Wang, H., Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 4068–4074 (2017)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Computer Science (2013)
Moghaddam, S., Ester, M.: Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews, pp. 1825–1828 (2010)
Nguyen, T.H., Shirai, K.: PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis. In: Conference on Empirical Methods in Natural Language Processing, pp. 2509–2514 (2015)
Tang, D., Qin, B., Feng, X., Liu, T.: Target-dependent sentiment classification with long short term memory. Computer Science (2015)
Tang, D., Qin, B., Liu, T.: Aspect Level Sentiment Classification with Deep Memory Network, pp. 214–224 (2016)
Vo, D.T., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. In: International Conference on Artificial Intelligence, pp. 1347–1353 (2015)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2017)
Wei, J., Ho, H.H.: A novel lexicalized hmm-based learning framework for web opinion mining. In: International Conference on Machine Learning, pp. 465–472 (2009). note from acm: A joint acm conference committee has determined that the authors of this article violated acm’s publication policy on simultaneous submissions. therefore acm has shut of
Acknowledgements
This work was supported by the National Natural Science Foundation of China U1636103, 61632011, Key Technologies Research and Development Program of Shenzhen JSGG20170817140856618, Shenzhen Foundational Research Funding 20170307150024907.
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Chen, G., Zhang, Q., Di Chen (2018). A Pair-Wise Method for Aspect-Based Sentiment Analysis. In: Xiao, J., Mao, ZH., Suzumura, T., Zhang, LJ. (eds) Cognitive Computing – ICCC 2018. ICCC 2018. Lecture Notes in Computer Science(), vol 10971. Springer, Cham. https://doi.org/10.1007/978-3-319-94307-7_2
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DOI: https://doi.org/10.1007/978-3-319-94307-7_2
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