Year: 2024
Type: article
Abstract: The analysis of audience emotional responses to textual content is vital across various fields, including politics, entertainment, industry, and research. Sentiment Analysis (SA), a branch of Natural Language Processing (NLP), employs statistical, lexical, and machine learning methods to predict audience emotions—neutral, positive, or negative—in response to diverse social media content. However, a notable research gap persists due to the lack of robust tools capable of quantifying features and independent text essential for assessing primary audience emotions within large-scale social media datasets. This study addresses the gap by introducing a novel approach to analyse the relationships within social media texts and evaluate audience emotions. A Dense Layer Graph (DLG-TF) model is proposed for textual feature analysis, enabling the exploration of intricate interconnections in the media landscape and enhancing emotion prediction capabilities. Social media data is processed using advanced convolutional network models, with emotion predictions derived from analysing textual features. Experimental results reveal that the DLG-TF model outperforms traditional emotion prediction techniques by delivering more accurate predictions across a broader emotional spectrum. Performance metrics, including accuracy, precision, recall, and F-measure, are assessed and compared against existing methodologies, demonstrating the superiority of the proposed model in utilizing social media datasets effectively
Source: Data & Metadata
DOI: 10.56294/dm2024.222
Institution: Koneru Lakshmaiah Education Foundation
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406495627