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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21307
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
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| Paper Title: | A Practical Machine Learning Approach to Real-Time Sentiment Analysis |
| Authors Name: | Dr.N Yamuna Devi , Prathicksha S , Preethi S T |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206189
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| Published Paper Id: | IJRTI2509085 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i9.206189 |
| Abstract: | The exponential growth of user-generated content on digital platforms has created a pressing need for efficient and interpretable sentiment analysis systems. This paper presents a machine learning–based real-time sentiment analysis framework designed to classify text into positive, negative, or neutral categories. Unlike deep learning systems that demand high computational power and often lack transparency, our approach integrates classical machine learning models with natural language processing (NLP) techniques to deliver lightweight yet accurate predictions. Text preprocessing includes tokenization, lemmatization, stopword removal, and feature representation through Term Frequency–Inverse Document Frequency (TF IDF). Models such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) were evaluated, with SVM achieving ~80% accuracy. To extend analytical value, non parametric hypothesis testing (Sign Test, Wilcoxon Signed-Rank Test, and Mann-Whitney U Test) was applied, and a real-time Streamlit interface was developed for deployment. The system balances accuracy, interpretability, and efficiency, making it suitable for applications in business analytics, social intelligence, and decision sciences. |
| Keywords: | Sentiment Analysis, Machine Learning, TF-IDF, Support Vector Machine, Streamlit, Text Analytics, Hypothesis Testing |
| Cite Article: | "A Practical Machine Learning Approach to Real-Time Sentiment Analysis ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a743-a750, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509085.pdf |
| Downloads: | 0001168 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2509085
Registration ID:206189
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i9.206189
Page No: a743-a750 Country: coimbatore, Tamil Nadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509085 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509085 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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