Abstract:
Sentiment analysis and mental health identification are critical areas in natural language processing (NLP) with significant implications for understanding human emotions and psychological states through text. Sentiment analysis focuses on categorizing text as positive, negative, or neutral, offering valuable insights into the overall sentiment and emotional tone expressed in the content. Mental health identification focuses on identifying mental state conditions, whether the person is depressed or anxious, from textual data, which can helpful in detecting early and treatment. This study shows the effectiveness of various natural processing models BERT, LSTM in sentiment analysis and mental health identification tasks. For sentiment analysis, our experiments demonstrated that BERT achieved a commendable accuracy of 97%, indicating its robust performance in understanding and classifying sentiment from textual data. In contrast, for mental health identification, where the goal was to identify mental health conditions based on textual input, LSTM emerged as the most accurate model, achieving a remarkable accuracy of 99%. This suggests that LSTM’s sequential learning capability is particularly well-suited for capturing complex patterns in mental health data. Our results underscore the strengths of these models in different contexts and highlight LSTM’s superiority in the domain of mental health identification.
