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@article{167264,
        author = {Mallela Vineela and N.Naveen Kumar},
        title = {Tweeting the Pandemic-A Machine Learning Approach to Identifying HIV/Aids Community Concerns during Covid-19},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {693-701},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167264},
        abstract = {The research addresses the critical concerns of People Living with HIV/AIDS (PLWHA) amidst the COVID-19 pandemic, aiming to understand the impact of the outbreak and its mitigation measures on this vulnerable population. Through sentiment analysis of tweets posted on social media platforms like Twitter, sentiments expressed by PLWHA are extracted and analyzed using tools like Vader Sentiment and TextBlob. The project involves comprehensive data exploration, preprocessing, and visualization techniques to gain insights into the sentiments expressed by PLWHA. Additionally, different machine learning models such as Support Vector Machine (SVM), Random Forest, and Decision Tree classifiers are employed to predict tweet polarity based on sentiments analyzed by Vader Sentiment and TextBlob. Furthermore, the study proposes the exploration of ensemble techniques like Voting Classifier to enhance model performance. As an extension, a front end using the Flask framework is proposed for user testing with authentication, facilitating a seamless and secure user experience. The findings shed light on the concerns of PLWHA during and post-pandemic, including issues such as high medical costs, late HIV diagnosis, limited access to medications, stigmatization, and lack of urgency in vaccine development.},
        keywords = {Sentiment analysis, thematic analysis, textual mining, tweets, HIV, AIDS, machine learning},
        month = {August},
        }