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@article{203955,
        author = {BHARATHNATH CHOWDARY KONANKI and Assoc. Prof. Mary selvan and Goutham G and Gokul Surya C and Gowri Sankar S},
        title = {DRUG REVIEW USECASE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2104-2111},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203955},
        abstract = {The widespread use of online healthcare platforms has led to the rapid growth of patient-generated drug reviews that provide insights into medication effectiveness, side effects, treatment satisfaction, and overall therapeutic outcomes. However, these reviews are primarily unstructured textual data, making systematic extraction of meaningful insights a complex task. This paper presents a Drug Review Analysis System that integrates Machine Learning (ML), Natural Language Processing (NLP), and Generative AI techniques to transform raw review data into structured analytical insights. The proposed framework performs multi-class sentiment classification to categorize reviews as positive, neutral, or negative, predicts patient medical conditions based on textual patterns, and estimates numerical drug ratings using regression models. In addition, Latent Dirichlet Allocation (LDA) is applied to identify dominant discussion themes such as effectiveness, adverse effects, dosage issues, and cost concerns. A Generative AI module enhances interpretability by generating concise summaries of lengthy reviews. The system supports pharmaceutical analysts and healthcare stakeholders in understanding patient perceptions and detecting negative feedback patterns through scalable, data- driven analysis.},
        keywords = {Drug Review Analysis, Sentiment Classification, Machine Learning, Topic Modeling, Generative AI},
        month = {June},
        }