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@article{154942,
        author = {R.Danush Vikram and K.S.Sudhishna and M.Lingeshraj and N.Gokulswaruban},
        title = {AI To Detect Social Media Users Depression Polarity Score and Diagnose Using Auto Curative Therapy},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1646-1649},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154942},
        abstract = {Depression is regarded as a major cause of global impairment and a leading cause of suicide. People are increasingly using social media to communicate their emotions these days. Sentiment Analysis (SA) is a computational tool for examining the polarity of emotions and ideas expressed in a text. We hope to forecast depressed individuals and quantify their depression intensity using social media (Twitter) data in this study, which will aid in sounding an alarm.},
        keywords = {SVM, KNN, Decision Tree, and Ensemble Learning have all been used in a few related studies. Some studies use a single set of features to identify depression in their posts, such as bag of words (BOW), N-grams, LIWC, or LDA.},
        month = {},
        }