Emotion-Based Sentiment Classification in Tweets Using RNN
Year: 2025
Type: article
Abstract: The rapid growth of social media platforms like Twitter has resulted in an abundance of user-generated content, making opinion mining a crucial task in understanding public sentiment. Sentiment analysis, a vital application of natural language processing (NLP), enables the categorization of emotions in textual data. This study presents a novel approach that utilizes NLP techniques and Recurrent Neural Networks (RNNs) to classify tweets into five emotion categories: angry, joy, happy, sad, and neutral. The proposed model demonstrates high accuracy and reliability, outperforming traditional methods in both binary and multi-class sentiment classification tasks. By leveraging the capabilities of deep learning, this method provides a robust framework for emotion recognition and sentiment analysis in real-world datasets. Keywords—Twitter,User-Generated Content, Sentiment Analysis, Natural Language Processing (NLP), Recurrent Neural Networks (RNNs), Emotion Categorization
DOI: 10.55041/ijsrem51095
Author: P. Jagannadha Varma
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411759486