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@article{180776,
        author = {Achint Goswami and Aditya Singh},
        title = {A Machine Learning Approach for Analyzing User Feedback and Product Reviews from Social Media},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2565-2569},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180776},
        abstract = {Understanding user feedback and product 
reviews expressed on social media has become essential 
for gauging public opinion, tracking consumer 
behavior, and identifying trends. However, the rise of 
bots, spam, and artificially generated content poses 
significant challenges to the reliability of this data. This 
research explores the use of logistic regression for 
classifying feedback and detecting bots, aiming to 
improve the accuracy and credibility of social media 
analysis. By applying feature extraction methods like 
TF-IDF, we were able to effectively classify review
related content with notable precision. Furthermore, 
integrating bot detection mechanisms helped filter out 
misleading or inauthentic data, ensuring that the 
insights drawn reflect genuine user feedback. The 
paper outlines the proposed approach, experimental 
framework, outcomes, and future potential of 
combining machine learning with social media analytics 
for more dependable feedback classification.},
        keywords = {user feedback analysis, logistic regression,  bot detection, social media, TF-IDF, machine learningg},
        month = {June},
        }