A Hybrid Deep Learning Approach for Sentiment Analysis of Dementia Care

Sentiment analysis plays a vital role in comprehending the emotions, viewpoints, and sentiments expressed in social media content, particularly in the domain of dementia care. Deep learning models have exhibited substantial potential across a range of natural language processing tasks, including sentiment analysis. In this paper, we introduce an innovative hybrid deep learning approach for analyzing sentiment in social media data related to dementia care. This method incorporates Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNNs) to capture both local and global dependencies present in Twitter data. To ensure a comprehensive understanding of the text, we employ FastText word embeddings, enabling a nuanced semantic interpretation of the dataset. Our experimental results unequivocally establish the superiority of this hybrid model when compared to prior methods, achieving an impressive accuracy rate of 80.34% in sentiment classification. The outcomes of this research provide valuable insights into the emotional expression within social media content associated with dementia care. Healthcare professionals and institutions can leverage these insights to gain a deeper understanding of the emotional states, attitudes, and viewpoints of the online community, ultimately facilitating the development of enhanced strategies for dementia care and elevating the overall quality of patient support and healthcare delivery.