Enhancing Sentiment Analysis in Text of Social Media Texts Using Hybrid Deep Learning Model and Natural Language Processing

Sentiment analysis (SA) is a mechanized strategy for finding and understanding the feelings depicted in text. Over the most recent decade, SA has altogether expanded in ubiquity in the Natural Language Processing (NLP) people group. One emotion that has a negative effect on people's daily life is depression. Every year, more people around the world report having long-lasting feelings. Finding persons with depression as early as possible is one of the most difficult difficulties. Researchers are analyzing text content posted on social media using Natural Language Processing (NLP) techniques, which helps to develop methods for Depression detection. This study examines various earlier investigations that employed learning strategies to recognize depression. The current approaches have issues with better model representation that make it difficult to accurately identify depression from literature. Fast message in the ongoing undertaking to address an answer for these issues, Fast text Convolution Neural Network with Long Momentary Memory (FCL), an original hybrid deep learning brain network plan with improved message portrayals, is made. Real-world datasets that were used in the literature were used to implement the current study. The suggested method achieves higher accuracy in detecting depression than the state-of-the-art.