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Joint sentence and aspect-level sentiment analysis of product comments

  • S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018
  • Published:
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Abstract

Comments from social media platforms (such as YouTube) have become a valuable resource for manufacturers to examine public opinion toward their products. Accordingly, we propose a novel framework for automatically collecting, filtering, and analyzing comments from YouTube for a given product. First, we devise a classification scheme to select relevant and high-quality comments from retrieval results. These comments are then analyzed in a sentiment analysis, where we introduce a joint approach to perform a combined sentence and aspect level sentiment analysis. Hence, we can achieve the following: (1) capture the mutual benefits between these two tasks, and (2) leverage knowledge learned from solving one task to solve another. Experiment results on our dataset show that the joint model achieves a satisfactory performance and outperforms the separate one on both sentence and aspect levels. Our framework does not require feature engineering efforts or external linguistic resources; therefore, it can be adapted for many languages without difficulties.

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Notes

  1. https://github.com/mailong25/sentiment-analysis.

  2. http://www.sentiment140.com/.

  3. https://hootsuite.com/.

  4. https://www.ibm.com/watson-analytics/.

  5. https://developers.google.com/youtube/v3/.

  6. https://detectlanguage.com/.

References

  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. In Proceedings of the workshop on languages in social media (pp. 30–38).

  • Alberto, T. C., Lochter, J. V., & e Almeida, T. A. (2015). Tubespam: Comment spam filtering on YouTube. In: Proceedings of the 14th international conference on machine learning and applications (pp. 138–143).

  • Alfaro, C., Cano-Montero, J., Gómez, J., Moguerza, J. M., & Ortega, F. (2016). A multi-stage method for content classification and opinion mining on weblog comments. Annals of Operations Research, 236(1), 197–213.

    Article  Google Scholar 

  • Ammari, A., Dimitrova, V., & Despotakis, D. (2011). Semantically enriched machine learning approach to filter YouTube comments for socially augmented user models. In Proceedings of workshop on augmented user models (pp. 6–17).

  • Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th annual meeting of the ACL (pp. 440–447).

  • Cho, K., Merrienboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259.

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

  • Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 1–14.

    Article  Google Scholar 

  • Filippova, K., & Hall, K. B. (2011). Improved video categorization from text metadata and user comments. In Proceedings of the 34th international ACM SIGIR (pp. 835–842).

  • Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. Stanford Digital Library Technologies Project: Technical report.

  • Hai, Z., Chang, K., & Kim, J. (2011). Implicit feature identification via co-occurrence association rule mining. In: Proceedings of the 12th international conference on computational linguistics and intelligent text processing (pp. 393–404).

  • Hercig, T., Brychcin, T., Svoboda, L., & Konkol, M. (2016). UWB at SemEval-2016 Task 5: Aspect based sentiment analysis. In Proceedings of SemEval-2016 (pp. 354–361).

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  • Hridoy, S. A. A., Ekram, M. T., Islam, M. S., Ahmed, F., & Rahman, R. M. (2015). Localized twitter opinion mining using sentiment analysis. Decision Analytics, 2(1), 1–19.

    Article  Google Scholar 

  • Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of SIGKDD’04 (pp. 168–177).

  • Irsoy, O., & Cardie, C. (2014). Opinion mining with deep recurrent neural networks. In Proceedings of EMNLP (pp. 720–728).

  • Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. arXiv:1412.6980

  • Krishna, A., Zambreno, J., & Krishnan, S. (2013). Polarity trend analysis of public sentiment on YouTube. In Proceedings of the 19th international conference on management of data (pp. 125–128).

  • Lafferty, J. D., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML (pp. 282–289).

  • Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural architectures for named entity recognition. In Proceedings of NAACL-2016 (pp. 260–270).

  • Liu, P., Joty, S., & Meng, H. (2015). Fine-grained opinion mining with recurrent neural networks and word embeddings. In Proceedings of EMNLP (pp. 1433–1443).

  • Long, C., Zhang, J., & Zhut, X. (2010). A review selection approach for accurate feature rating estimation. In Proceedings of the 23rd international conference on computational linguistics (pp. 766–774).

  • Mai, L., & Le, B. (2018). Aspect-based sentiment analysis of vietnamese texts with deep learning. In 11th Asian conference on intelligent information and database systems (pp. 149–158).

  • Marrese-Taylor, E., Balazs, J. A., & Matsuo, Y. (2017). Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN. In Proceedings of the 8th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 102–111).

  • Mishra, N., & Singh, A. (2018). Use of twitter data for waste minimisation in beef supply chain. Annals of Operations Research, 270(1–2), 337–359.

    Article  Google Scholar 

  • Mitchell, M., Aguilar, J., Wilson, T., & Durme, B. V. (2013). Open domain targeted sentiment. In Proceedings of EMNLP (pp. 1643–1654).

  • Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv:1802.05365.

  • Schultes, P., Dorner, V., & Lehner, F. (2013). Leave a comment! An in-depth analysis of user comments on YouTube. Wirtschaftsinformatik, 42, 659–673.

    Google Scholar 

  • Schuster, M., & Paliwal, K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.

    Article  Google Scholar 

  • Severyn, A., Moschitti, A., Uryupina, O., Plank, B., & Filippova, K. (2014). Opinion mining on YouTube. In Proceedings of the 52nd annual meeting of the ACL (pp. 1252–1261).

  • Severyn, A., Moschitti, A., Uryupina, O., Plank, B., & Filippova, K. (2016). Multi-lingual opinion mining on YouTube. Information Processing and Management, 52(1), 46–60.

    Article  Google Scholar 

  • Siersdorfer, S., Chelaru, S., Nejdl, W., & Pedro, J. S. (2010). How useful are your comments? Analyzing and predicting YouTube comments and comment ratings. In Proceedings of World Wide Web (pp. 891–900).

  • Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of EMNLP (pp. 1631–1642).

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.

    Google Scholar 

  • Toh, Z., & Su, J. (2016). NLANGP at SemEval-2016 Task 5: Improving aspect based sentiment analysis using neural network features. In Proceedings of SemEval-2016 (pp. 282–288).

  • Uryupina, O., Plank, B., Severyn, A., Rotondi, A., & Moschitti, A. (2014). Sentube: A corpus for sentiment analysis on YouTube social media. In Proceedings of the ninth international conference on language resources and evaluation (pp. 4244–4249).

  • Xenos, D., Theodorakakos, P., Pavlopoulos, J., Malakasiotis, P., & Androutsopoulos, I. (2016). AUEB-ABSA at SemEval-2016 task 5: Ensembles of classifiers and embeddings for aspect based sentiment analysis. In Proceedings of SemEval-2016 (pp. 312–317).

  • Yuan, H., Xu, W., Li, Q., & Lau, R. (2018). Topic sentiment mining for sales performance prediction in e-commerce. Annals of Operations Research, 270(1–2), 553–576.

    Article  Google Scholar 

  • Zhang, M., Zhang, Y., & Vo, D. (2015). Open domain targeted sentiment. In Proceedings of EMNLP (pp. 612–621).

  • Zhu, J., Wang, H., Tsou, B. K., & Zhu, M. (2009). Multi-aspect opinion polling from textual reviews. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 1799–1802).

  • Zhuang, L., Jing, F., & Zhu, X. (2006). Movie review mining and summarization. In Proceedings of CIKM (pp. 43–50).

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Authors and Affiliations

  1. Department of Computer Science, University of Science, Ho Chi Minh City, Vietnam

    Long Mai & Bac Le

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Long Mai & Bac Le

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  1. Long Mai
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  2. Bac Le
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Correspondence to Bac Le.

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Mai, L., Le, B. Joint sentence and aspect-level sentiment analysis of product comments. Ann Oper Res 300, 493–513 (2021). https://doi.org/10.1007/s10479-020-03534-7

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  • DOI: https://doi.org/10.1007/s10479-020-03534-7

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