Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.
Abstract & Details
Research Area
Data Science
Keywords
Hybrid Language Processing
Sentiment Analysis
Contextual Analysis
Natural Language Processing (NLP)
Real-Time Data Processing
Social Media Analytics
Machine Learning
Rule-Based Systems
Transformer Models (BERT
GPT
etc.)
Text Mining
Sarcasm Detection
Opinion Mining
Emotion Detection
Abstract
Social media platforms generate vast amounts of unstructured data in real-time, which includes diverse expressions, slang, and informal language. Traditional Natural Language Processing (NLP) models face challenges in accurately interpreting such content. This research focuses on developing a hybrid language processing system combining rule-based methods, machine learning, and deep learning techniques for real-time sentiment and contextual analysis. The proposed system aims to achieve high accuracy by integrating the precision of rule-based systems, the adaptability of machine learning, and the contextual depth of deep learning models like BERT. It also addresses challenges such as sarcasm, idiomatic expressions, and regional slang, ensuring contextual understanding while retaining scalability for real-time data processing. The research demonstrates its applicability in areas like brand monitoring, crisis detection, and customer feedback analysis, showcasing the system's versatility across industries. Future directions include expanding capabilities to support multimodal analysis and multiple languages, ensuring ethical practices, and improving computational efficiency for global applicability
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Chetan Gopal Patil | Dr. D. Y. Patil College Of Arts, Commerce and Sciences. |
| 2 | Neekha Susan Sabu | Dr. D. Y. Patil College Of Arts, Commerce and Sciences. |
| 3 | Pruthviraj Kishor Chavan | Dr. D. Y. Patil College Of Arts, Commerce and Sciences. |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Patil, Chetan Gopal, Sabu, Neekha Susan, & Chavan, Pruthviraj Kishor (2025). Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 846-851.
MLA Style
Patil, Chetan Gopal, et al. "Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 846-851.
IEEE Style
Chetan Gopal Patil, Neekha Susan Sabu, and Pruthviraj Kishor Chavan, "Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 846-851, 2025.
Vancouver Style
Patil Chetan Gopal, Sabu Neekha Susan, Chavan Pruthviraj Kishor. Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):846-851.
Harvard Style
Patil, Chetan Gopal, Sabu, Neekha Susan, & Chavan, Pruthviraj Kishor (2025) 'Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 846-851.
Chicago Style
Patil, Chetan Gopal, Neekha Susan Sabu, and Pruthviraj Kishor Chavan. "Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 846-851.
Turabian Style
Patil, Chetan Gopal, Neekha Susan Sabu, and Pruthviraj Kishor Chavan. "Hybrid Language Processing for Real-Time Sentiment and Contextual Analysis in Social Media Platforms.." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 846-851.
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