Survey on Detection of Metal Illnesses by Analysing Twitter Data - Sundarrajan, Aksharaa; Aneesha, M (International Journal of Engineering and Technology, 2018)
Mental illnesses are serious problems that places a burden on individuals, their families and on society in general. Although their symptoms have been known for several years, accurate and quick diagnoses remain a challenge. Inaccurate or delayed diagnoses results in increased frequency and severity of mood episodes, and reduces the benefits of treatment. In this survey paper, we review papers that leverage data from social media and design predictive models. These models utilize patterns of speech and life features of various subjects to determine the onset period of bipolar disorder. This is done by studying the patients, their behaviour, moods and sleeping patterns, and then effectively mapping these features to detect whether they are currently in a prodromal phase before a mood episode or not.Â