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Mental well-being and social media have been closely related domains ofstudy. In this research a novel model, AD prediction model, for anxiousdepression prediction in real-time tweets is proposed. This mixedanxiety-depressive disorder is a predominantly associated with erratic thoughtprocess, restlessness and sleeplessness. Based on the linguistic cues and userposting patterns, the feature set is defined using a 5-tuple vector <word,timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built todetect the presence of anxiety indicators. Time and frequency of tweet isanalyzed for irregularities and opinion polarity analytics is done to findinconsistencies in posting behaviour. The model is trained using threeclassifiers (multinomial na\"ive bayes, gradient boosting, and random forest)and majority voting using an ensemble voting classifier is done. Preliminaryresults are evaluated for tweets of sampled 100 users and the proposed modelachieves a classification accuracy of 85.09%.

Anxious Depression Prediction in Real-time Social Data