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@article{166720,
        author = {Aishwarya Nagaraj Naik and Dr. B Meenakshi Sundaram},
        title = {Sentiment Analysis in Social Media},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1882-1892},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166720},
        abstract = {The World Wide Web generates vast amounts of data reflecting users' views, emotions, and opinions on various topics, significantly influencing readers, vendors, and politicians. Platforms like Facebook, WhatsApp, and Twitter are inundated with such data, which can be transformed into valuable information through sentiment analysis. This method classifies sentiments as negative, positive, favorable, or unfavorable, but it faces challenges due to a lack of labeled data. To address this, sentiment analysis and machine learning techniques are combined. In a project, a sentiment analysis system for Twitter data was developed using a Random Forest classifier and a Flask web application. The system preprocesses tweets by removing URLs, HTML tags, and special characters, and converts text data into numerical features using TF-IDF vectorization. The classifier was trained and evaluated, demonstrating effective performance in classifying sentiments. The system, deployed as a web application, allows users to input tweets and receive real-time sentiment predictions. This project showcases the practical implementation of sentiment analysis, detailing data preprocessing, feature extraction, model building, and deployment, and highlights the potential of machine learning models in analyzing social media data.},
        keywords = {Sentiment analysis; recurrent neural network; deep neural network; convolutional neural network; recursive neural network},
        month = {July},
        }