A Robust Hybrid Model for Text and Emoji Sentiment Analysis: Leveraging BERT and Pre-trained Emoji Embeddings - Arjun Kuruva; Dr. C. Nagaraju (The Bioscan, 2025)
Sentiment analysis, a critical subfield of natural language processing, is widely employed to decipher the emotions and opinions expressed in textual data. With the growing prevalence of emojis in digital communication, understanding their contribution alongside textual information has become paramount for comprehensive sentiment classification. This paper proposes a novel hybrid deep learning model for sentiment analysis that effectively integrates advanced feature fusion and attention mechanisms to address the challenges of analyzing multimodal data. By combining BERT-based textual embeddings and pre-trained emoji embeddings, the model captures nuanced semantic and emotional information. A self-attention mechanism further enhances the representation by identifying long-range dependencies and contextual relationships between text and emojis. The model was evaluated on the Sentiment140 dataset, achieving state-of-the-art performance with an accuracy of 91.7%, an F1-score of 93.6%, and an AUC of 96.4%, outperforming existing models such as BERT-LSTM and RoBERTa-GRU. This superior performance demonstrates the effectiveness of multimodal fusion in sentiment classification, particularly for social media data where emojis play a significant role in emotional expression. The proposed architecture also shows strong generalizability, offering robust performance across diverse datasets. While computational complexity is a noted challenge, future research could explore optimization techniques to improve efficiency without compromising accuracy. This work highlights the potential of hybrid models to advance sentiment analysis by bridging the gap between textual and visual- emotional communication, setting a foundation for more comprehensive multimodal understanding in natural language processing tasks.