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Machine Learning Techniques Based on Sentiment Analysis for Drug Recommendation System
Abstract
A recommended system can help the user make sense of complex information and make well-considered choices. User-generated material is conveyed in human language in various nuanced ways, making it difficult to provide recommendations based on a study of attitudes. However, there has been a shortage of research devoted to health and medical concerns compared to more mainstream areas like product and media critiques and dining-out recommendations. Sentiment analysis (SA) of the healthcare industry as a whole and patient medication experiences has the potential to yield important information on where to focus efforts to enhance public health. Studies in natural language processing and machine learning are working on performing efficient and trustworthy data SA as more and more people publish their opinions online. In this review, we describe an SA-based drug recommendation system using different machine learning techniques. The review paper provides the SA and its different level, approaches, need, and advantages, the recommendation system, their classification techniques, and the machine learning techniques that help accurately predict sentiments of drug reviews. The primary content of these analyses is a commentary on the efficacy and adverse effects of various medications. It is essential not only to find a technique to measure a treatment's efficiency but also to determine the specific disease in which the medicine has its beneficial, neutral, or detrimental effects.
Keywords
References
Battineni G, Sagaro GG, Chintalapudi N, Di Canio M, Amenta F. Assessment of awareness and knowledge on novel coronavirus (COVID-19) pandemic among seafarers. Healthcare. 2021; 9 (2): 120.
Goh JM, Gao G, Agarwal R. The creation of social value. MIS Q. 2016; 40 (1): 247–264.
Cook SF, Bies RR. Disease progression modeling: key concepts and recent developments. Curr Pharmacol Rep. 2016; 2: 221–230.
Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA, August 24–27, 2008. pp. 426–434.
Ye Q, Hsieh CY, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T. A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun. 2021; 12 (1): Article 6775.
Lu J, Wu D, Mao M, Wang W, Zhang G. Recommender system application developments: a survey. Decis Support Syst. 2015; 74: 12–32.
Huang F, Wang S, Chan CC. Predicting disease by using data mining based on healthcare information system. In: 2012 IEEE International Conference on Granular Computing, Hangzhou, China, August 11–13, 2012. pp. 191–194.
Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathi D, Senthilselvan N. An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput. 2019; 75: 3184–3216.
Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowledge Data Eng. 2005; 17 (6): 734–749.
Esfandiari N, Babavalian MR, Moghadam AM, Tabar VK. Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl. 2014; 41 (9): 4434–4463.
Mukund SR, Suma NR. Drug recommendation system. Int Adv Res J Sci Eng Technol. 2022; 9 (6): 729–734.
Hussein DM. A survey on sentiment analysis challenges. J King Saud Univ Eng Sci. 2018; 30 (4): 330–338.
Ahmad SR, Yusop NM, Asri AM, Amran MF. A review of feature selection algorithms in sentiment analysis for drug reviews. Int J Adv Computer Sci Appl. 2021; 12 (12): 126–132.
Feldman R. Techniques and applications for sentiment analysis. Commun ACM. 2013; 56 (4): 82–89.
Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Prague, Czech Republic, July 6, 2002. pp. 79–86.
Wiebe J, Bruce R, O’Hara TP. Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, College Park, MD, USA, June 20–26, 1999. pp. 246–253.
Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, August 22–25, 2004. pp. 168–177.
Mittal A, Patidar S. Sentiment analysis on twitter data: a survey. In: Proceedings of the 7th International Conference on Computer and Communications Management, Bangkok, Thailand, July 27–29, 2019. pp. 91–95.
Chen T, Su P, Shang C, Hill R, Zhang H, Shen Q. Sentiment classification of drug reviews using fuzzy-rough feature selection. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA, June 23–26, 2019 . pp. 1–6.
Lakshmi SS, Lakshmi TA. Recommendation systems: issues and challenges. Int J Computer Sci Inform Technol. 2014; 5 (4): 5771–5772.
Kohar M, Rana C. Survey paper on recommendation system. Int J Computer Sci Inform Technol. 2012; 3 (2): 3460–3462.
Alhijawi B, Kilani Y. The recommender system: a survey. Int J Adv Intell Paradigms. 2020; 15 (3): 229–251.
Sharma M, Mann S. A survey of recommender systems: approaches and limitations. Int J Innov Eng Technol. 2013; 2 (2): 8–14.
Stark B, Knahl C, Aydin M, Elish K. A literature review on medicine recommender systems. Int J Adv Computer Sci Appl. 2019; 10 (8): 6–13.
IBM Watson Health is Now Merative. [Online]. Imaging Technology News. 2022. Available at https://www.itnonline.com/content/ibm-watson-health-now-merative
Hemmatian F, Sohrabi MK. A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev. 2019; 52 (3): 1495–1545.
da Silva NF, Coletta LF, Hruschka ER, Hruschka ER Jr. Using unsupervised information to improve semi-supervised tweet sentiment classification. Inform Sci. 2016; 355: 348–365.
Tekade TN, Emmanuel M. Probabilistic aspect mining approach for interpretation and evaluation of drug reviews. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi, India, October 3–5, 2016. pp. 1471–1476.
Narducci F, Musto C, Polignano M, de Gemmis M, Lops P, Semeraro G. A recommender system for connecting patients to the right doctors in the healthnet social network. In: Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, May 18–22, 2015. pp. 81–82.
Han Q, Ji M, de Troya IM, Gaur M, Zejnilovic L. A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, October 1–4, 2018. pp. 481–490.
Bhimavarapu U, Chintalapudi N, Battineni G. A fair and safe usage drug recommendation system in medical emergencies by a stacked ANN. Algorithms. 2022; 15 (6): 186.
Garg S. Drug recommendation system based on sentiment analysis of drug reviews using machine learning. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, January 28–29, 2021. pp. 175–181.
Punith NS, Raketla K. Sentiment analysis of drug reviews using transfer learning. In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, September 2–4, 2021. pp. 1794–1799.
Hossain MD, Azam MS, Ali MJ, Sabit H. Drugs rating generation and recommendation from sentiment analysis of drug reviews using machine learning. In: 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), Dhaka, Bangladesh, December 21–22, 2020. pp. 1–6.
Joshi S, Abdelfattah E. Multi-class text classification using machine learning models for online drug reviews. In: 2021 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, May 10–13, 2021. pp. 0262–0267.
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