Abstract:
With the increasing popularity of social media as a platform for sharing thoughts, ideas, and opinions, a massive amount of unstructured data, such as user posts, is being generated. To effectively process this unstructured data, supervised learning methods are preferred, leading to improved performance optimization. In healthcare applications, the complexity of the data has motivated the adoption of Deep Learning (DL) approaches, including Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models. DL techniques excel in analyzing large volumes of healthcare data, uncovering hidden patterns, and extracting valuable information that traditional analytics methods struggle to achieve within reasonable timeframes. In this paper, we investigate DL models for text categorization in social media healthcare networks, focusing on leveraging natural language processing (NLP) techniques to preprocess and train the data. Our primary goal is to enhance the performance of the text classifier by improving accuracy and processing speed, ultimately generating findings with high potential for future success.
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