Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{178126,
        author = {S. Harish kumar and ASAN NAINAR.M},
        title = {SENTIMENT ANALYSIS OF ONLINE SOCIAL REVIEWS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2859-2866},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178126},
        abstract = {This project, titled "Sentiment Analysis of Online social Reviews," focuses on analyzing textual feedback provided by customers on online social products. The objective is to develop a system capable of automatically classifying user reviews based on their sentiment—such as positive, negative, or neutral. Leveraging techniques from Natural Language Processing (NLP) and machine learning, the system provides a comprehensive sentiment analysis pipeline that processes raw textual data to produce insightful classifications. The project utilizes TextBlob for polarity detection, converting text into numerical features using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization. Multiple machine learning models, including Logistic Regression, Random Forest, Naive Bayes, and Support Vector Machines (SVM), are employed to classify sentiments into categories such as "Positive," "Negative," "Neutral," as well as varying levels of sentiment intensity like "Strongly Positive" or "Weakly Negative."
The performance of each model is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The system also features graphical visualizations such as bar plots, word clouds, and confusion matrices to help understand sentiment distributions and model performance. This automated sentiment analysis system provides a valuable tool for businesses to gain insights into customer opinions, helping them enhance product offerings and customer satisfaction. The system is efficient, cost-effective, and can be extended to handle large datasets, making it a robust solution for analyzing customer feedback in real-world applications.},
        keywords = {},
        month = {May},
        }