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Aspect-Based Sentiment Analysis for User Reviews

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Abstract

Aspect-based sentiment analysis (ABSA) can help consumers provide clear and objective sentiment recommendations through massive quantities of data and is conducive to overcoming ambiguous human weaknesses in subjective judgments. However, the robustness and accuracy of existing sentiment analysis methods must still be improved. We first propose a deep-level semiself-help sentiment annotation system based on the bidirectional encoder representation from transformers (BERT) weakly supervised classifier to address this problem. Fine-grained annotation of restaurant reviews under 18 latitudes solves the problems of insufficient data and low label accuracy. On this basis, bagging traditional machine learning algorithms and annotation systems, a novel classification model for specific aspects is proposed to explore consumer behavior preferences, real consumer feelings, and whether they are willing to consume again. The proposed approach can effectively improve the accuracy of the ABSA tasks and reduce the space-time complexity. Moreover, the proposed model can significantly reduce the quantity of data annotation engineering required.

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Notes

  1. https://mp.weixin.qq.com/s/W0PhbE8149nD3Venmy33tw

References

  1. Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7.

    Article  Google Scholar 

  2. Akhtar MS, Ekbal A, Bhattacharyya P. Aspect based sentiment analysis: category detection and sentiment classification for hindi. In International Conference on Intelligent Text Processing and Computational Linguistics. Springer 2016. pp. 246–257.

  3. Liu B. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. 2012;5(1):1–167.

    Article  Google Scholar 

  4. Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval. 2008. 2(1–2):1–135.

  5. Yang Q, Rao Y, Xie H, Wang J, Wang FL, Chan WH, Cambria EC. Segment-level joint topic-sentiment model for online review analysis. IEEE Intell Syst. 2019;34(1):43–50.

    Article  Google Scholar 

  6. Zhang S, Zhong H. Mining users trust from e-commerce reviews based on sentiment similarity analysis. IEEE Access. 2019;7:13523–35.

    Article  Google Scholar 

  7. Strååt B, Verhagen H, Warpefelt H. Probing user opinions in an indirect way: an aspect based sentiment analysis of game reviews. In Proceedings of the 21st International Academic Mindtrek Conference. 2017. pp. 1–7.

  8. Strååt B, Verhagen H. Using user created game reviews for sentiment analysis: A method for researching user attitudes. In GHITALY@ CHItaly. 2017.

  9. Zhu M, Fang X. A lexical analysis of nouns and adjectives from online game reviews. In International Conference on Human-Computer Interaction. Springer 2015. pp. 670–680.

  10. Cambria E, Poria S, Hazarika D, Kwok K. Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceedings of the AAAI Conference on Artificial Intelligence. 2018. volume 32.

  11. Huang F, Wei K, Weng J, Li Z. Attention-based modality-gated networks for image-text sentiment analysis. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 2020. 16(3):1–19.

  12. Huettner A, Subasic P. Fuzzy typing for document management. ACL 2000 Companion Volume: Tutorial Abstracts and Demonstration Notes. 2000. pp. 26–27.

  13. Bravo-Marquez F, Frank E, Pfahringer B. Building a twitter opinion lexicon from automatically-annotated tweets. Knowl Based Syst. 2016;108:65–78.

    Article  Google Scholar 

  14. Papineni K. Why inverse document frequency? In Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies. Assoc Comput Linguist. 2001. pp. 1–8.

  15. Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293–300.

    Article  Google Scholar 

  16. Stevenson RA, Mikels JA, James TW. Characterization of the affective norms for english words by discrete emotional categories. Behav Res Methods. 2007;39(4):1020–4.

    Article  Google Scholar 

  17. Cambria E, Li Y, Xing FZ, Poria S, Kwok K. Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020. pp. 105–114.

  18. Lunter G. Hmmoc-a compiler for hidden markov models. Bioinformatics. 2007;23(18):2485–7.

    Article  Google Scholar 

  19. Sarawagi S, Cohen WW. Semi-markov conditional random fields for information extraction. In Advances in Neural Information Processing Systems. 2005. pp. 1185–1192.

  20. Kadari R, Zhang Y, Zhang W, Liu T. Ccg supertagging via bidirectional lstm-crf neural architecture. Neurocomputing. 2018;283:31–7.

    Article  Google Scholar 

  21. Sak H, Senior AW, Beaufays F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. 2014.

  22. Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. Abcdm: An attention-based bidirectional cnn-rnn deep model for sentiment analysis. Future Gener Comput Syst. 2021;115:279–94.

    Article  Google Scholar 

  23. El-Affendi MA, Alrajhi K, Hussain A. A novel deep learning-based multilevel parallel attention neural (mpan) model for multidomain arabic sentiment analysis. IEEE Access. 2021;9:7508–18.

    Article  Google Scholar 

  24. Akhtar MS, Ekbal A, Cambria E. How intense are you? predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Comput Intell Mag. 2020;15(1):64–75.

    Article  Google Scholar 

  25. Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint 2014. arXiv:1412.3555.

  26. Li X, Bing L, Li P, Lam W. A unified model for opinion target extraction and target sentiment prediction. Proc Conf AAAI Artif Intell. 2019;33:6714–21.

    Google Scholar 

  27. Li X, Bing L, Zhang W, Lam W. Exploiting bert for end-to-end aspect-based sentiment analysis. arXiv preprint 2019. arXiv:1910.00883.

  28. Aisopos F, Papadakis G, Tserpes K, Varvarigou T. Content vs. context for sentiment analysis: a comparative analysis over microblogs. In Proceedings of the 23rd ACM conference on Hypertext and social media. 2012. pp. 187–196.

  29. Tang J, Lu Z, Su J, Ge Y, Song L, Sun L, Luo J. Progressive self-supervised attention learning for aspect-level sentiment analysis. arXiv preprint 2019. arXiv:1906.01213.

  30. Wang J, Li J, Li S, Kang Y, Zhang M, Si L, Zhou G. Aspect sentiment classification with both word-level and clause-level attention networks. IJCAI. 2018;2018:4439–45.

    Google Scholar 

  31. Majumder N, Bhardwaj R, Poria S, Zadeh A, Gelbukh A, Hussain A, Morency L-P. Improving aspect-level sentiment analysis with aspect extraction. arXiv preprint 2020. arXiv:2005.06607.

  32. Dashtipour K, Gogate M, Li J, Jiang F, Kong B, Hussain A. A hybrid persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing. 2020;380:1–10.

    Article  Google Scholar 

  33. Ma Y, Peng H, Khan TM, Cambria E, Hussain A. Sentic lstm: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput. 2018;10(4):639–50.

    Article  Google Scholar 

  34. Devlin J, Chang M-W, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint 2018. arXiv:1810.04805.

  35. Ramadhan W, Novianty SA, Setianingsih SC. Sentiment analysis using multinomial logistic regression. In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC). IEEE 2017. pp. 46–49.

  36. Chen T, He T. Benesty m. xgboost: Extreme gradient boosting. R package version 0.4-4. 2016.

  37. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint 2013. arXiv:1301.3781.

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Funding

This work was supported by the National Key R&D Program of China (No. 2020YFB1006002).

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, 4 E 2nd Section, 1st Ring Rd, Jianshe Road, Chenghua District, Chengdu, China

    Yin Zhang

  2. Zhongnan University of Economics and Law, Wuhan, China

    Jinyang Du, Xiao Ma & Haoyu Wen

  3. University of Calabria, Via Pietro Bucci, Rende CS, 87036 Arcavacata, Italy

    Giancarlo Fortino

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  1. Yin Zhang
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  2. Jinyang Du
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  5. Giancarlo Fortino
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Correspondence to Yin Zhang.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

Jinyang Du, Yin Zhang, Xiao Ma, Haoyu Wen and Giancarlo Fortino declare that they have no conflicts of interest.

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Zhang, Y., Du, J., Ma, X. et al. Aspect-Based Sentiment Analysis for User Reviews. Cogn Comput 13, 1114–1127 (2021). https://doi.org/10.1007/s12559-021-09855-4

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