Abstract
Aspect-based sentiment analysis (ABSA) of patients’ opinions expressed in drug reviews can extract valuable information about specific aspects of a particular drug such as effectiveness, side effects and patient conditions. One of the most important and challenging tasks of ABSA is to extract the implicit and explicit aspects from a text, and to classify the extracted aspects into predetermined classes. Supervised learning algorithms possess high accuracy in extracting and classifying aspects; however, they require annotated datasets whose manual construction is time-consuming and costly. In this paper, first a new method was introduced for identifying expressions that indicate an aspect in user reviews about drugs in English. Then, distant supervision was adopted to automate the construction of a training set using sentences and phrases that are annotated as aspect classes in the drug domain. The results of the experiments showed that the proposed method is able to identify various aspects of the test set with 74.4% F-measure, and outperforms the existing aspect extraction methods. Also, training the random forest classifier on the dataset that was constructed via distant supervision obtained the F-measure of 73.96%, and employing this dataset to fine-tune BERT for aspect classification yielded better F-measure (78.05%) in comparison to an existing method in which the random forest classifier trained on an accurate manually constructed dataset.
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We used Weka and Stanford CoreNLP tools for performing machine learning algorithms and processing of the text.
References
Afzaal, M, Usman, M, & Fong, A (2019). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65(2), 233–242. https://doi.org/10.1109/TCE.2019.2908944.
Al-Smadi, M, Al-Ayyoub, M, Jararweh, Y, & et al. (2019). Enhancing aspect-based sentiment analysis of arabic hotels’ reviews using morphological, syntactic and semantic features. Information Processing & Management, 56(2), 308–319. https://doi.org/10.1016/j.ipm.2018.01.006.
Bancken, W, Alfarone, D, & Davis, J (2014). Automatically detecting and rating product aspects from textual customer reviews. In Proceedings of the 1st international workshop on interactions between data mining and natural language processing at ECML/PKDD, CEUR-WS. org, pp 1–16.
Benkhelifa, R, Bouhyaoui, N, & Laallam, FZ (2019). A real-time aspect-based sentiment analysis system of youtube cooking recipes. In Machine Learning Paradigms: Theory and Application. Springer, pp 233–251.
Bhamare, B R, & Prabhu, J (2021). A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas. PeerJ Computer Science, 7, e347. https://doi.org/10.7717/peerj-cs.347.
Bodenreider, O (2004). The unified medical language system (umls): integrating biomedical terminology. Nucleic Acids Research, 32(suppl_1), D267–D270. https://doi.org/10.1093/nar/gkh061.
Cavalcanti, D, & Prudêncio, R (2017a). Aspect-based opinion mining in drug reviews. In EPIA Conference on Artificial Intelligence, Springer, pp 815–827.
Cavalcanti, DC, & Prudêncio, RB (2017b). Unsupervised aspect term extraction in online drugs reviews. In The Thirtieth International Flairs Conference.
Chauhan, G S, Meena, Y K, Gopalani, D, & et al. (2020). A two-step hybrid unsupervised model with attention mechanism for aspect extraction. Expert Systems with Applications, 161, 113,673. https://doi.org/10.1016/j.eswa.2020.113673.
Dai, H, & Song, Y. (2019). Neural aspect and opinion term extraction with mined rules as weak supervision. arXiv:190703750.
Da’u, A, & Salim, N (2019). Aspect extraction on user textual reviews using multi-channel convolutional neural network. PeerJ Computer Science, e191, 5. https://doi.org/10.7717/peerj-cs.191.
Devlin, J, Chang, MW, Lee, K, & et al. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:181004805.
Do, H H, Prasad, P, Maag, A, & et al. (2019). Deep learning for aspect-based sentiment analysis: a comparative review. Expert Systems with Applications, 118, 272–299. https://doi.org/10.1016/j.eswa.2018.10.003.
Firmanto, A, & Sarno, R (2019). Aspect-based sentiment analysis using grammatical rules, word similarity and senticircle. International Journal of Intelligent Engineering and Systems, 12(5), 190–201. https://doi.org/10.22266/ijies2019.1031.19.
Giannakopoulos, A, Antognini, D, Musat, C, & et al. (2017). Dataset construction via attention for aspect term extraction with distant supervision. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 373–380.
Gräßer, F, Kallumadi, S, Malberg, H, & et al (2018). Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In Proceedings of the 2018 international conference on digital health, pp 121–125.
Han, Y, Liu, M, & Jing, W (2020). Aspect-level drug reviews sentiment analysis based on double bigru and knowledge transfer. IEEE Access 8:21, 325, 314–21. https://doi.org/10.1109/ACCESS.2020.2969473.
Kim, K (2018). An improved semi-supervised dimensionality reduction using feature weighting: application to sentiment analysis. Expert Systems with Applications, 109, 49–65. https://doi.org/10.1016/j.eswa.2018.05.023.
Kuhn, M, Letunic, I, Jensen, L J, & et al. (2016). The sider database of drugs and side effects. Nucleic Acids Research, 44(D1), D1075–D1079. https://doi.org/10.1093/nar/gkv1075.
Liu, B (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016.
Liu, H, Chatterjee, I, Zhou, M, & et al. (2020). Aspect-based sentiment analysis: a survey of deep learning methods. IEEE Transactions on Computational Social Systems, 7(6), 1358–1375. https://doi.org/10.1109/TCSS.2020.3033302.
Manning, CD, Surdeanu, M, Bauer, J, & et al (2014). The stanford corenlp natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–60.
Miller, G A (1995). Wordnet: a lexical database for english. Communications of the ACM, 38(11), 39–41. https://doi.org/10.1145/219717.219748.
Mintz, M, Bills, S, Snow, R, & et al (2009). Distant supervision for relation extraction without labeled data. In Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, pp 1003–1011.
Noferesti, S, & Shamsfard, M (2015). Using linked data for polarity classification of patients’ experiences. Journal of Biomedical Informatics, 57, 6–19. https://doi.org/10.1016/j.jbi.2015.06.017.
Poria, S, Cambria, E, & Gelbukh, A (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42–49. https://doi.org/10.1016/j.knosys.2016.06.009.
Rana, T A, & Cheah, Y N (2016). Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 46(4), 459–483.
Ray, P, & Chakrabarti, A. (2019). A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. appl. Comput Inform https://doi.org/10.1016/j.aci.2019.02.002.
Samha, A K, & Li, Y (2016). Aspect-based opinion mining using dependency relations. Int J Comput Sci Trends Technol (IJCST), 4, 113–123.
Shafie, A S, Sharef, N M, Murad, M A A, & et al. (2018). Aspect extraction performance with pos tag pattern of dependency relation in aspect-based sentiment analysis. In 2018 4th International conference on information retrieval and knowledge management (CAMP), IEEE, pp 1–6.
Tajudeen, F P, Jaafar, N I, & Ainin, S (2018). Understanding the impact of social media usage among organizations. Information & Management, 55 (3), 308–321. https://doi.org/10.1016/j.im.2017.08.004.
Tarnowska, K A, & Ras, Z (2021). Nlp-based customer loyalty improvement recommender system (clirs2). Big Data and Cognitive Computing, 5(1), 4. https://doi.org/10.3390/bdcc5010004.
Tarnowska, KA, & Ras, ZW (2019). Sentiment analysis of customer data. In Web Intelligence, IOS Press, pp 343–363 https://doi.org/10.3233/WEB-190423.
Tran, TU, Hoang, HTT, & Huynh, HX (2020). Bidirectional independently long short-term memory and conditional random field integrated model for aspect extraction in sentiment analysis. In Frontiers in intelligent computing: Theory and applications. Springer, pp 131–140 https://doi.org/10.1007/978-981-13-9920-6_14.
Tubishat, M, Idris, N, & Abushariah, M A (2018). Implicit aspect extraction in sentiment analysis: Review, taxonomy, oppportunities, and open challenges. Information Processing & Management, 54(4), 545–563.
Xu, H, Liu, B, Shu, L, & et al. (2019). Bert post-training for review reading comprehension and aspect-based sentiment analysis. arXiv:190402232.
Yang, Y, Chen, C, Qiu, M, & et al (2017). Aspect extraction from product reviews using category hierarchy information. In Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Volume 2, Short Papers, pp 675–680.
Zhang, L, & Liu, B (2014). Aspect and entity extraction for opinion mining. In Data mining and knowledge discovery for big data. Springer, pp 1–40.
Zheng, X, Lin, Z, Wang, X, & et al. (2014). Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification. Knowledge-Based Systems, 61, 29–47. https://doi.org/10.1016/j.knosys.2014.02.003.
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Mostafa Imani: Conception and design of the study, implementation of the proposed method, evaluation of the experimental results, drafting the article. Samira Noferesti: Conception and design of the study, evaluation of the experimental results, editing and revising the article, supervision.
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Imani, M., Noferesti, S. Aspect extraction and classification for sentiment analysis in drug reviews. J Intell Inf Syst 59, 613–633 (2022). https://doi.org/10.1007/s10844-022-00712-w
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DOI: https://doi.org/10.1007/s10844-022-00712-w