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Hybrid Attention-Based Deep Learning Model for Enhanced Sentiment Analysis in Noisy Social Media Data: A Context-Aware Approach

Authors: Narendra Kumar

Published: 2025-05-02 11:49:57.045

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Abstract

Social media platforms have become crucial sources of information for understanding public opinion. However, the inherent noisiness and contextual complexity of social media data pose significant challenges for accurate sentiment analysis. This paper presents a novel hybrid attention-based deep learning model designed to address these challenges. Our approach combines the strengths of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), augmented with multiple attention mechanisms, to effectively capture both local and global contextual information. The model also incorporates a pre-processing module for noise reduction and normalization. Extensive experiments on benchmark datasets demonstrate that our proposed model outperforms state-of-the-art sentiment analysis techniques, particularly in handling noisy and contextually ambiguous social media text. The results highlight the effectiveness of the hybrid architecture and attention mechanisms in capturing nuanced sentiment expressions, contributing to more accurate and robust sentiment analysis systems.

Keywords

Sentiment Analysis, Deep Learning, Attention Mechanism, Social Media, Noise Reduction, Contextual Embedding, Hybrid Model, Natural Language Processing, Text Classification, Machine Learning

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How to Cite

Narendra Kumar, (2025-05-02 11:49:57.045). Hybrid Attention-Based Deep Learning Model for Enhanced Sentiment Analysis in Noisy Social Media Data: A Context-Aware Approach. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 4.

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