Table of Content

Advance Machine Learning for Sentiment Analysis over Various Domains and Applications

Submission Deadline: 23 November 2023 Submit to Special Issue

Guest Editors

Dr. Muhammad Asif, National Textile University, Pakistan.
Dr. Kemal Polat, Bolu Abant Izzet Baysal University, Turkey.
Dr. Osama Sohaib, American University of Ras Al Khaimah, UAE.

Summary

Dear Colleagues, nowadays due to the massive amount of digital data on different social platforms it has become very necessary to closely monitor that data produced in terms of tweets, comments, blogs writing, etc. Many countries and important organizations like defense, security, advertising agencies, feedback on different products, the popularity of politicians, extremism detection need state of the art sentiment analysis methods to uncover the hidden factors from these sentiments of people. This special issue will focus on the potential state of the art methods and techniques to uncover the patterns and knowledge from the tweets, comments and blogs. The main target of this special issue will be to present the research articles related to deep learning, transfer learning, RNN based models, CNN-based models, Hybrid neural network models, techniques in Natural Language Processing (NLP) and related areas. The issue will accept the papers related to the following areas.

 

Machine learning for sentiment analysis.

NLP for sentiment analysis.

Sentiment analysis for extremisms detection.

Sentiment analysis for product reviews.

Sentiment analysis for threat detections.

Multi-domain Sentiment Classification.

Aspect-based sentiment analysis.

Deep learning for sentiment analysis.

NN-based approach for sentiment analysis.

Syntax-Aware Aspect-Term Sentiment Analysis.

Graph-based methods.

Visualization of the sentiment aspects.

Span-Based Models for SA.

Real-Time Sentiment Analysis

Any related methods and techniques for uncovering the facts from sentiments.


Keywords

Machine learning; sentiment analysis; NLP; extremisms detection; product reviews.

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