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An Empirical Study on Sentimental Drug Review Analysis Using Lexicon and Machine Learning-Based Techniques

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Abstract

In today's age of intense digital revolution, a considerable proportion of perceptions and judgments are conveyed regularly on commodities, such as drug-related products across consumer reviews and other social networking sites. Patients or the general public typically posts them and an excellent significant source for examining the emotions related to numerous drugs. This research aims to investigate the emotions through online medicinal comments and categorize them into positive, negative, and neutral. Sentiment analysis is conducted on testimonials of patients to anticipate the patient's aggregate level of satisfaction with pharmaceutical and medicinal drugs for being either favorable or adverse. The emotions were characterized using various vectorization methods and employing machine and ensemble learning classifiers. The SVC outperforms other algorithms and yields the best classification matrices. However, the Random Forest classifier, on the other hand, achieves the lowest classification matrices of all the classifiers implemented.

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Data Availability

The dataset is publicly available at the UCI Machine Learning Repository.

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The authors declare they did not receive any sort of funding to support this research.

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Authors and Affiliations

  1. Central University of Punjab, Bathinda, India

    Aaqib Iqbal Alaie, Umar Farooq, Wakeel Ahmad Bhat, Surinder Singh Khurana & Parvinder Singh

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  1. Aaqib Iqbal Alaie
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  2. Umar Farooq
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  3. Wakeel Ahmad Bhat
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  4. Surinder Singh Khurana
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  5. Parvinder Singh
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Correspondence to Umar Farooq.

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Alaie, A.I., Farooq, U., Bhat, W.A. et al. An Empirical Study on Sentimental Drug Review Analysis Using Lexicon and Machine Learning-Based Techniques. SN COMPUT. SCI. 5, 63 (2024). https://doi.org/10.1007/s42979-023-02384-x

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