Social Media Sentiment Analyzer

The Social Media Sentiment Analyzer is a real-time web-based tool designed to assess public opinions across social media platforms. Built using Python and Streamlit, the system employs natural language processing (NLP) and machine learning algorithms including Logistic Regression, Random Forest, and Neural Networks. It performs sentiment classification into positive, negative, and neutral classes using TF-IDF feature extraction. The tool provides interactive dashboards for real-time visualization and helps individuals and businesses monitor sentiment trends effectively. The system is scalable, customizable, and open-source, with support for integration into existing data workflows and potential for multilingual adaptation

Authors

Publication Details

Journal
International Journal of Innovative Research in Engineering
Published
2025-08-28
DOI
https://doi.org/10.59256/ijire.20250604008
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
0.00
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article

Social Media Sentiment Analyzer

Abbas Mohammed, Khaja Mahabubullah
International Journal of Innovative Research in Engineering
Sentiment Analysis and Opinion Mining
article

Social Media Sentiment Analyzer

Abbas Mohammed, Khaja Mahabubullah
article en

Abstract

The Social Media Sentiment Analyzer is a real-time web-based tool designed to assess public opinions across social media platforms. Built using Python and Streamlit, the system employs natural language processing (NLP) and machine learning algorithms including Logistic Regression, Random Forest, and Neural Networks. It performs sentiment classification into positive, negative, and neutral classes using TF-IDF feature extraction. The tool provides interactive dashboards for real-time visualization and helps individuals and businesses monitor sentiment trends effectively. The system is scalable, customizable, and open-source, with support for integration into existing data workflows and potential for multilingual adaptation

International Journal of Innovative Research in Engineering
Openalex Percentile: Top 88%
Sentiment Analysis and Opinion Mining
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