Soft computing and natural language processinSoft computing and natural language processing to determine the physiological behaviour for analysing mental disorders
Keywords:
Psychological Analysis, Language Schema, Machine learning, Formal Schema, Translation SkillsAbstract
Understanding mental health problems and their root causes relies heavily on the interpretation of psychological behavior. This research emphasizes the potential of employing natural language generation (NLG) and machine learning (ML) techniques, such as BERT (Bidirectional Encoder Representations from Transformers) and LDA (Latent Dirichlet Allocation), to identify various psychological behaviors, including depression, loneliness, anxiety, and normal behavior. By scrutinizing and comprehending linguistic features, encompassing syntactic, semantic, and psychological aspects, the algorithms demonstrated remarkable accuracy in predicting psychological behavior and mental disorders. These findings hold significant implications for the field of psychology and mental health by offering novel tools to detect and comprehend psychological disorders. In essence, this study underscores the importance of utilizing advanced computational techniques like NLG and ML to analyze intricate data for determining the mental health of individuals. This approach facilitates new insights into human behavior and aids in developing more precise and effective interventions for psychological disorders. The capability to interpret psychological behavior holds crucial implications for mental health and well-being. Given the prevalence of mental health issues in society, early detection and treatment play a pivotal role in enhancing outcomes. This has motivated us for this research study. By analyzing and interpreting psychological behavior using NLG and ML techniques, the proposed research assists in identifying the early warning signs of mental health problems by understanding the psychological behavior of the target audience.
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