Multiclass Emoji Prediction: Integrating BERT and CNN-BiLSTM for Improved Performance

The natural language processing (NLP) field has been profoundly inclined to emoji prediction because of its potential to enhance digital communication by giving text-based interactions more emotional depth and meaning. Emojis are little visual symbols that help with nonverbal communication in electronic communication. They also decrease ambiguity and increase user happiness. This study resolves the problem of the context-independent embeddings utilized in earlier emoji prediction approaches, which dictates model performance by failing to capture intricate details of the context within words. To overcome this issue, we suggest a novel technique that creates rich contextual embeddings by utilizing Bidirectional Encoder Representations from Transformers (BERT). A CNN-BiLSTM model is then used to process these embeddings, successfully capturing the text’s spatial and sequential connections. Our approach significantly outperformed previous models by 9-0.2% accuracy by integrating the advantages of CNN and BiLSTM architectures. The study has significant real-world implications since precise emoji prediction can improve user experience in applications like chatbots, social media platforms, and sentiment analysis tools by facilitating more contextually relevant and emotionally complex interactions. Our model opens up options for more complex developments in context-aware NLP systems and sets a new benchmark in the field.