Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model

Somiya Rani; Amita Jain

doi:10.1007/s11042-023-16360-3

journal article Aug 07, 2023

Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model

Somiya Rani Amita Jain

Multimedia Tools and Applications Vol. 83 No. 8 pp. 22473-22501 · Springer Science and Business Media LLC

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References

44

[1]

Abdelgwad MM, Soliman TH, Taloba AI, Farghaly MF (2022) Arabic aspect based sentiment analysis using bidirectional GRU based models. J King Saud Univ-Comput Inf Sci 34(9):6652–6662. https://doi.org/10.48550/arXiv.2101.10539 10.48550/arxiv.2101.10539

[2]

Alsayat A (2022) Improving sentiment analysis for social media applications using an ensemble deep learning language model. Arab J Sci Eng 47(2):2499–2511. https://doi.org/10.1007/s13369-021-06227-w 10.1007/s13369-021-06227-w

[3]

Basiri ME, Abdar M, Cifci MA, Nemati S, Acharya UR (2020) A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowl-Based Syst 198:105949. https://doi.org/10.1016/j.knosys.2020.105949 10.1016/j.knosys.2020.105949

[4]

Bayraktar K, Yavanoglu U, Ozbilen A (2019) A rule-based holistic approach for Turkish aspect-based sentiment analysis. In: 2019 IEEE International Conference on Big Data (Big Data) pp 2154–2158. https://doi.org/10.1109/BigData47090.2019.9005473 10.1109/bigdata47090.2019.9005473

[5]

Bensoltane R, Zaki T (2022) Towards Arabic aspect-based sentiment analysis: A transfer learning-based approach. Soc Netw Anal Min 2:1–6. https://doi.org/10.1007/s13278-021-00794-4 10.1007/s13278-021-00794-4

[6]

Bhatti UA, Wu G, Bazai SU, Nawaz SA, Baryalai M, Bhatti MA, Hasnain A, Nizamani MM (2022) A Pre-to Post-COVID-19 change of air quality patterns in anhui province using path analysis and regression. Pol J Environ Stud 31(5). https://doi.org/10.15244/pjoes/148065 10.15244/pjoes/148065

[7]

Bonifazi G, Breve B, Cirillo S, Corradini E, Virgili L (2022) Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach. Inf Process Manag 59(6):103095. https://doi.org/10.1016/j.ipm.2022.103095 10.1016/j.ipm.2022.103095

[8]

Chen J, Tam D, Raffel C, Bansal M, Yang D (2013) An empirical survey of data augmentation for limited data learning in NLP. Trans Assoc Comput Linguist 11:191–211. https://doi.org/10.1162/tacl_a_00542 10.1162/tacl_a_00542

[9]

Comparing deep learning architectures for sentiment analysis on drug reviews

Cristóbal Colón-Ruiz, Isabel Segura-Bedmar

Journal of Biomedical Informatics 2020 10.1016/j.jbi.2020.103539

[10]

Dubey G, Singh HP, Sheoran K, Dhand G, Malik P (2023) Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN-RNN. Concurr Comput Pract Exp 35:e7512. https://doi.org/10.1002/cpe.7512 10.1002/cpe.7512

[11]

Geetha MP, Renuka DK (2021) Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model. Int J Intell Netw 2:64–69. https://doi.org/10.1016/j.ijin.2021.06.005 10.1016/j.ijin.2021.06.005

[12]

Gräßer F, Kallumadi S, Malberg H, Zaunseder S (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 https://doi.org/10.1145/3194658.3194677 10.1145/3194658.3194677

[13]

Han Y, Liu M, Jing W (2020) Aspect-level drug reviews sentiment analysis based on double BiGRU and knowledge transfer. IEEE Access 8:21314–21325. https://doi.org/10.1109/ACCESS.2020.2969473 10.1109/access.2020.2969473

[14]

He R, Lee WS, Ng HT, Dahlmeier D (2018) Exploiting document knowledge for aspect-level sentiment classification. arXiv preprint arXiv:1806.04346. https://doi.org/10.48550/arXiv.1806.04346 10.48550/arxiv.1806.04346

[15]

Huang B, Ou Y, Carley KM (2018) Aspect level sentiment classification with attention-over-attention neural networks. In: Social, Cultural, and Behavioral Modeling: 11th International Conference, SBP-BRiMS 2018, Washington, DC, USA, Proceedings 11 2018 (pp 197–206). Springer International Publishing. https://doi.org/10.48550/arXiv.1804.06536 10.48550/arxiv.1804.06536

[16]

Huang S, Huang M, Zhang Y, Chen J, Bhatti U (2020) Medical image segmentation using deep learning with feature enhancement. IET Image Process 14(14):3324–3332. https://doi.org/10.1049/iet-ipr.2019.0772 10.1049/iet-ipr.2019.0772

[17]

Jayanto R, Kusumaningrum R, Wibowo A (2022) Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory. Int J Adv Intell Inform 8(3):391–403. https://doi.org/10.26555/ijain.v8i3.691 10.26555/ijain.v8i3.691

[18]

Jiménez-Zafra SM, Martín-Valdivia MT, Molina-González MD, Ureña-López LA (2019) How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for the medical domain. Artif Intell Med 93:50–57. https://doi.org/10.1016/j.artmed.2018.03.007 10.1016/j.artmed.2018.03.007

[19]

Karsi R, Zaim M, El Alami J (2021) Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews. Rev d'Intelligence Artif 35(4):307–314. https://doi.org/10.18280/ria.350405 10.18280/ria.350405

[20]

Ke Z, Sheng J, Li Z, Silamu W, Guo Q (2021) Knowledge-guided sentiment analysis via learning from natural language explanations. IEEE Access 9:3570–3578. https://doi.org/10.1109/ACCESS.2020.3048088 10.1109/access.2020.3048088

[21]

Li X, Bing L, Zhang W, Lam W (2019) Exploiting BERT for end-to-end aspect-based sentiment analysis https://doi.org/10.48550/arXiv.1910.00883 10.48550/arxiv.1910.00883

[22]

Lin Y, Wang C, Song H, Li Y (2021) Multi-head self-attention transformation networks for aspect-based sentiment analysis. IEEE Access 9:8762–8770. https://doi.org/10.1109/ACCESS.2021.3049294 10.1109/access.2021.3049294

[23]

Liu Q, Zhang H, Zeng Y, Huang Z, Wu Z (2018) Content attention model for aspect based sentiment analysis. In: Proceedings of the 2018 world wide web conference pp 1023–1032. https://doi.org/10.1145/3178876.3186001 10.1145/3178876.3186001

[24]

Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893. https://doi.org/10.48550/arXiv.1709.00893 10.48550/arxiv.1709.00893

[25]

Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI conference on artificial intelligence, vol. 32, no. 1. https://doi.org/10.1609/aaai.v32i1.12048 10.1609/aaai.v32i1.12048

[26]

Ma Y, Peng H, Khan T, Cambria E, Hussain A (2018) Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput 10:639–650. https://doi.org/10.1007/s12559-018-9549-x 10.1007/s12559-018-9549-x

[27]

Miao YL, Cheng WF, Ji YC, Zhang S, Kong YL (2021) Aspect-based sentiment analysis in Chinese based on mobile reviews for BiLSTM-CRF. J Intell Fuzzy Syst 40(5):8697–8707. https://doi.org/10.3233/JIFS-192078 10.3233/jifs-192078

[28]

The Chip Is the Network: Toward a Science of Network-on-Chip Design

Radu Marculescu, Paul Bogdan

Foundations and Trends® in Electronic Design Autom... 2008 10.1561/1500000011

[29]

Patil RS, Kolhe SR (2022) Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets. Soc Netw Anal Min 12(1):51. https://doi.org/10.1007/s13278-022-00877-w 10.1007/s13278-022-00877-w

[30]

Ray P, Chakrabarti A (2022) A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Appl Comput Inform 18(1/2):163–178. https://doi.org/10.1016/j.aci.2019.02.002 10.1016/j.aci.2019.02.002

[31]

Setiawan EI, Ferry F, Santoso J, Sumpeno S, Fujisawa K, Purnomo MH (2020) Bidirectional GRU for targeted aspect-based sentiment analysis based on character-enhanced token-embedding and multi-level attention. Computing. 1:2. https://doi.org/10.22266/ijies2020.1031.35 10.22266/ijies2020.1031.35

[32]

Sun C, Huang L, Qiu X (2019) Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence https://doi.org/10.48550/arXiv.1903.09588 10.48550/arxiv.1903.09588

[33]

Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

Asmaa Hashem Sweidan, Nashwa El-Bendary, Haytham Al-Feel

IEEE Access 2021 10.1109/access.2021.3091394

[34]

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 pp 214–224, Austin, Texas. Association for Computational Linguistics. https://doi.org/10.48550/arXiv.1605.08900 10.48550/arxiv.1605.08900

[35]

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 pp 606–615. https://doi.org/10.18653/v1/D16-1058 10.18653/v1/d16-1058

[36]

Individual Comparisons by Ranking Methods

Frank Wilcoxon

Biometrics Bulletin 1945 10.2307/3001968

[37]

Xiao L, Xue Y, Wang H, Hu X, Gu D, Zhu Y (2022) Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks. Neurocomputing 471:48–59. https://doi.org/10.1016/j.neucom.2021.10.091 10.1016/j.neucom.2021.10.091

[38]

Xu H, Liu B, Shu L, Yu PS (2019) BERT post-training for review reading comprehension and aspect-based sentiment analysis. https://doi.org/10.48550/arXiv.1904.02232 10.48550/arxiv.1904.02232

[39]

Xue W, Li T (2018) Aspect based sentiment analysis with gated convolutional networks https://doi.org/10.48550/arXiv.1805.07043 10.48550/arxiv.1805.07043

[40]

Yadav S, Ekbal A, Saha S, Bhattacharyya P (2018) Medical sentiment analysis using social media: Towards building a patient assisted system. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)

[41]

Yang C, Zhang H, Jiang B, Li K (2019) Aspect-based sentiment analysis with alternating coattention networks. Inf Process Manag 56(3):463–478. https://doi.org/10.1016/j.ipm.2018.12.004 10.1016/j.ipm.2018.12.004

[42]

Zhao A, Yu Y (2021) Knowledge-enabled BERT for aspect-based sentiment analysis. Knowl-Based Syst 227:107220. https://doi.org/10.1016/j.knosys.2021.107220 10.1016/j.knosys.2021.107220

[43]

Zhao H, Liu Z, Yao X, Yang Q (2021) A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Inf Process Manag 58(5):102656. https://doi.org/10.1016/j.ipm.2021.102656 10.1016/j.ipm.2021.102656

[44]

Žunić A, Corcoran P, Spasić I (2022) The case of aspect in sentiment analysis: seeking attention or co-dependency? Mach Learn Knowl Extr 4(2):474–487 10.3390/make4020021

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Published: Aug 07, 2023
Vol/Issue: 83(8)
Pages: 22473-22501
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Cite This Article

Somiya Rani, Amita Jain (2023). Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model. Multimedia Tools and Applications, 83(8), 22473-22501. https://doi.org/10.1007/s11042-023-16360-3

Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model

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