Depression Detection Model Based on Sentiment Analysis on Twitter API

Nithin P, Owais Ahmed, Aditya Jason Hans, Amaan Faraaz, Prof. Madhusudhan M.V

social media, API, data mining, classification, depression.

Datasets derived from social networks are useful in a number of areas, including sociology and psychology. However, technical assistance is inadequate, and precise approaches are desperately needed. Our project uses data mining in the field of psychology to classify depressed consumers of twitter. To begin, a sentiment analysis approach is proposed that uses vocabulary and man-made rules to measure social media’s tendency (twitter). Second, a depression identification model is developed using the proposed approach and ten depressed consumer characteristics derived from psychological studies. The model is then tested using three different forms of classifiers. The relevance of each function is also investigated. Finally, within the proposed model, a tool for online mental health monitoring is created. Some psychologists endorse this research, which helps them boost their data centric approach to analysing the impact of major events.