Detecting anxiety based on social interaction and sentiment analysis in social media

  • Miss. Smita. S. Wagh, Prof. P.P.Joshi


Today, the large development and amount of online social networks and their features, laterally through the big amount of socially linked users, it’s become problematic to clarify the semantic worth of available content for the discovery of user behavior and anxiety. We propose a platform for analyzing the content of social media that finds anxiety for analyzing and detecting abnormal behavior that deviates meaningfully from the standard in big - scale community systems. Different Categories of analysis have been perform for an improved knowledge of various user behaviors in detecting highly adaptive anxiety users. We propose a new method to the data extraction and classification process to contextualize big-scale networks appropriately as well as collect a significant number of user profiles from the activities of Facebook and Instagram. Comprehensive assessments were made of real-world data sets of user activity for both social networks.