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Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

Author(s)
Suraya Mubeen Department of ECE, CMR Technical Campus, Hyderabad, India
Nandini Kulkarni Symbiosis School of Planning Architecture and Design, Symbiosis International Deemed University, India
Manuel R. Tanpoco Department of Decision Sciences and Innovation, De La Salle University, Manila, Philippines
R.Dinesh Kumar Department of Electronics and Communication Engineering, PERI Institute of Technology, Chennai, Tamil Nadu, India
Lakshmu Naidu M, Department of Electronics and communication Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India.
Tanuja Dhope Electronics and Communication, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, India.
Abstract
A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset
Publication Details
Page(s) 176-186
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 3, Year: 2022

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Keywords
Social Media, emotion detection, kernel component analysis, Keywords:linguistic, metaheuristic deep learning
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