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A span-based model for aspect terms extraction and aspect sentiment classification

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Abstract

Sentiment analysis is a field of natural language processing, which is used to identify and extract opinions and attitudes from text. Aspect-based sentiment analysis aims to extract aspect terms and predict sentiment categories of the opinion aspects. It includes two subtasks: aspect terms extraction and aspect sentiment classification. However, previous studies regarded them as two independent tasks and solve them, respectively, which has limitations for practical application. In this paper, we combine the requirements of two subtasks to propose a new aspect-based sentiment analysis framework based on span, which is a simple and effective joint model to generate all aspects and corresponding sentiment polarities of the input sentences. Specifically, dual gated recurrent units which are used to extract the respective representation of each task can process sequence information better, and an interaction layer which is used to consider the relationship between the representations. Experiments on three benchmark datasets show that the proposed framework outperforms the state-of-the-art baseline models.

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References

  1. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 168-177. https://doi.org/10.1145/1014052.1014073

  2. Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107. https://doi.org/10.1109/MIS.2016.31

    Article  Google Scholar 

  3. Hussain A, Cambria E (2018) Semi-supervised learning for big social data analysis. Neurocomputing 275:1662–1673. https://doi.org/10.1016/j.neucom.2017.10.010

    Article  Google Scholar 

  4. Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdiscip Rev Data Min Knowl Discov. https://doi.org/10.1002/widm.1253

    Article  Google Scholar 

  5. Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011), Target-dependent twitter sentiment classification. In: The 49th annual meeting of the association for computational linguistics: human language technologies, pp 151–160

  6. Liu K, Xu HL, Liu Y, Zhao J (2013) Opinion target extraction using partially-supervised word alignment model. In: Proceedings of the 23rd international joint conference on artificial intelligence, IJCAI 2013, IJCAI/AAAI, pp 2134–2140

  7. Wang W, Pan SJ, Dahlmeier D, Xiao X (2016) Recursive neural conditional random fields for aspect-based sentiment analysis. In: Proceedings of the 2016 Conference on empirical methods in natural language processing, EMNLP 2016, ACL, pp 616–626. https://doi.org/10.18653/v1/d16-1059

  8. Yin Y, Wei F, Dong L, Xu K, Zhang M, Zhou M (2016) Unsupervised word and dependency path embeddings for aspect term extraction. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI/AAAI Press, pp 2979–2985

  9. Li X, Bing L, Li P, Lam W, Yang Z (2018) Aspect term extraction with history attention and selective transformation. In Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI 2018, ijcai.org, pp 4194–4200. https://doi.org/10.24963/ijcai.2018/583

  10. Nagarajan SM, Gandhi UD (2019) Classifying streaming of twitter data based on sentiment analysis using hybridization. Neural Comput Appl 31(5):1425–1433. https://doi.org/10.1007/s00521-018-3476-3

    Article  Google Scholar 

  11. Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 conference on empirical methods in natural language processing, ACL, pp 214–224. https://doi.org/10.18653/v1/D16-1021

  12. Li X, Bing L, Lam W, Shi B (2018) Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Vol 1: Long Papers), ACL, pp 946–956. https://doi.org/10.18653/v1/P18-1087

  13. Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3433–3442. https://doi.org/10.18653/v1/D18-1380

  14. Mitchell M, Aguilar J, Wilson T, Van Durme B (2013) Open domain targeted sentiment. In: Proceedings of the 2013 conference on empirical methods in natural language processing, ACL, pp 1643–1654

  15. Zhang M, Zhang Y, Vo D (2015) Neural networks for open domain targeted sentiment. In: Proceedings of the 2015 conference on empirical methods in natural language processing, association for computational linguistics, pp 612–621. https://doi.org/10.18653/v1/d15-1073

  16. Li X, Bing L, Li P, Lam W (2019) A unified model for opinion target extraction and target sentiment prediction. In: The thirty-third AAAI conference on artificial intelligence, AAAI 2019, the thirty-first innovative applications of artificial intelligence conference, IAAI 2019, the ninth AAAI symposium on educational advances in artificial intelligence, EAAI 2019, pp 6714–6721. https://doi.org/10.1609/aaai.v33i01.33016714

  17. Luo H, Li T, Liu B, Zhang J (2019) DOER: dual cross-shared RNN for aspect term-polarity co-extraction. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, vol 1: Long Papers, pp 591–601. https://doi.org/10.18653/v1/p19-1056

  18. Lee K, Kwiatkowski T, Parikh AP, Das D (2016) Learning recurrent span representations for extractive question answering. CoRR abs/1611.01436. http://arxiv.org/abs/1611.01436, 1611.01436

  19. Zhou Y, Huang L, Guo T, Han J, Hu S (2019) A span-based joint model for opinion target extraction and target sentiment classification. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, ijcai.org, pp 5485–5491. https://doi.org/10.24963/ijcai.2019/762

  20. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019, vol 1 (Long and Short Papers), pp 4171–4186. https://doi.org/10.18653/v1/n19-1423

  21. Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32(6):74–80. https://doi.org/10.1109/MIS.2017.4531228

    Article  Google Scholar 

  22. Xia C, Zhang C, Yang T, Li Y, Du N, Wu X, Fan W, Ma F, Yu PS (2019) Multi-grained named entity recognition. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, vol 1: Long Papers, ACL, pp 1430–1440. https://doi.org/10.18653/v1/p19-1138

  23. Xia Y, Cambria E, Hussain A, Zhao H (2015) Word polarity disambiguation using Bayesian model and opinion-level features. Cogn Comput 7(3):369–380. https://doi.org/10.1007/s12559-014-9298-4

    Article  Google Scholar 

  24. Poria S, Cambria E, Gelbukh AF (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49. https://doi.org/10.1016/j.knosys.2016.06.009

    Article  Google Scholar 

  25. Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of AAAI-18, IAAI-18, EAAI-18. pp 5876–5883. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16541

  26. He R, Lee WS, Ng HT, Dahlmeier D (2017) An unsupervised neural attention model for aspect extraction. In: Proceedings of the 55th annual meeting of the association for computational linguistics, ACL 2017, vol 1: Long Papers, pp 388–397. https://doi.org/10.18653/v1/P17-1036

  27. Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the international conference on web search and web data mining, WSDM 2008, ACM, pp 231–240. https://doi.org/10.1145/1341531.1341561

  28. Awwalu J, Bakar AA, Yaakub MR (2019) Hybrid n-gram model using Naïve Bayes for classification of political sentiments on twitter. Neural Comput Appl 31(12):9207–9220. https://doi.org/10.1007/s00521-019-04248-z

    Article  Google Scholar 

  29. Ramesh A, Kumar SH, Foulds JR, Getoor L (2015) Weakly supervised models of aspect-sentiment for online course discussion forums. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian federation of natural language processing, ACL 2015, vol 1: Long Papers, pp 74–83. https://doi.org/10.3115/v1/p15-1008

  30. Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, EMNLP 2016, ACL, pp 606–615. https://doi.org/10.18653/v1/d16-1058

  31. He L, Lee K, Levy O, Zettlemoyer L (2018) Jointly predicting predicates and arguments in neural semantic role labeling. In: Proceedings of the 56th annual meeting of the association for computational linguistics, ACL, pp 364–369. https://www.aclweb.org/anthology/P18-2058/

  32. Lee K, He L, Lewis M, Zettlemoyer L (2017) End-to-end neural coreference resolution. In: Proceedings of the 2017 conference on empirical methods in natural language processing, ACL, pp 188–197. https://doi.org/10.18653/v1/d17-1018

  33. Lee K, He L, Zettlemoyer L (2018) Higher-order coreference resolution with coarse-to-fine inference. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT, Vol 2 (Short Papers), ACL, pp 687–692. https://doi.org/10.18653/v1/n18-2108

  34. Hu M, Peng Y, Huang Z, Li D, Lv Y (2019) Open-domain targeted sentiment analysis via span-based extraction and classification. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Vol 1: Long Papers, pp 537–546. https://doi.org/10.18653/v1/p19-1051

  35. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Lu, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems 30, Curran Associates, Inc., pp 5998–6008

  36. Cho K, van Merrienboer B, Gülçehre,cehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, ACL, pp 1724–1734. https://doi.org/10.3115/v1/d14-1179

  37. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  38. Józefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: Proceedings of the 32nd international conference on machine learning, ICML 2015, JMLR.org, JMLR workshop and conference proceedings, vol 37, pp 2342–2350. http://proceedings.mlr.press/v37/jozefowicz15.html

  39. Zhang H, Li J, Ji Y, Yue H (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inf 13(2):616–624. https://doi.org/10.1109/TII.2016.2601521

    Article  Google Scholar 

  40. Seo MJ, Kembhavi A, Farhadi A, Hajishirzi H (2017) Bidirectional attention flow for machine comprehension. In: 5th international conference on learning representations, ICLR 2017, OpenReview.net, https://openreview.net/forum?id=HJ0UKP9ge

  41. Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Semeval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th international workshop on semantic evaluation, SemEval@COLING 2014, pp 27–35. https://doi.org/10.3115/v1/s14-2004

  42. Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I (2015) Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th international workshop on semantic evaluation, SemEval@NAACL-HLT 2015, pp 486–495. https://doi.org/10.18653/v1/s15-2082

  43. Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, Clercq OD, Hoste V, Apidianaki M, Tannier X, Loukachevitch NV, Kotelnikov EV, Bel N, Zafra SMJ, Eryigit G (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation, SemEval@NAACLHLT 2016, ACL, pp 19–30. https://doi.org/10.18653/v1/s16-1002

  44. Liu L, Shang J, Ren X, Xu FF, Gui H, Peng J, Han J (2018) Empower sequence labeling with task-aware neural language model. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), pp 5253–5260. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17123

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 61901099 and 61876205).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Yanxia Lv, Fangna Wei & Ying Zheng

  2. School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China

    Yanxia Lv, Cong Wang, Cong Wan & Cuirong Wang

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  1. Yanxia Lv
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  2. Fangna Wei
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Correspondence to Fangna Wei.

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Lv, Y., Wei, F., Zheng, Y. et al. A span-based model for aspect terms extraction and aspect sentiment classification. Neural Comput & Applic 33, 3769–3779 (2021). https://doi.org/10.1007/s00521-020-05221-x

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