Efficient Model for Social Behaviour Analysis and Depression Detection in User Tweets

Knowing the way individuals engage on social networking platforms has become important in the modern era. The current research discusses the urgent need for advanced methods for analyzing tweets from users In order to analyze social behavior, particularly when it comes to the recognition of sadness. Though sentiment evaluation has advanced, current techniques frequently disregard crucial variables like user age, tweet time, and emotion of users-linked data. precisely a consequence, there remains a shortage in the understanding needs to correctly assess depression-related behaviors based upon social networking content. We offer a novel architecture that combines fuzzy logic constraint algorithms, graph neural network models (GNNs), with the VADER sentiment detection in order to bridge this gap. Our approach takes into account contextual variables including the age of the individual and the date and time of the post so as to determine the mood of the user's tweets via VADER sentiment evaluation. likewise, our team apply GNNs for further sentiment analysis to linked information, allowing for greater understanding of the feelings of users. When integrating these inputs, the fuzzy logic rule generator provides the user with an anxiety class, delivering the detailed viewpoint upon mental wellness assessments. With the incorporation combined specific to users’ characteristics including sentiment assessment of linked data, our model arrives at enhanced accuracy in predicting overall anxiety. furthermore, the incorporation of fuzzy logic improves accessibility as well as flexibility in response to many different user inputs. This work provides significant possibilities regarding specific networks of support as well as mental wellness measures, in addition to developing the area of psychological analysis. In simple terms, the research we performed presents a comprehensive strategy for understanding and fixing emotional wellness problems in today's technological world.