Advancing Multimodal Sentiment and Emotion Detection: Exploring Text Analysis with Hybrid ALBERT - BiLSTM Model

A major paradigm shift has occurred in the options for multimodal reception of sentiment analysis and emotion detection, with the aid of application of deep learning, different types of data including speech inputs, texts, facial expressions, and physiological measures. This paper presents a novel approach to text analysis, leveraging a hybrid ALBERT-BiLSTM model to capture sentiment and emotion information. By combining the contextualized word representations of ALBERT with the sequence modeling capabilities of BiLSTM, this hybrid model achieves state-of-the-art performance in sentiment and emotion detection. Experimental results demonstrate the effectiveness of this approach, providing insights into the importance of text analysis in multimodal sentiment and emotion detection. This research contributes to the development of more accurate and robust multimodal analysis frameworks.