sandeshregmi / anapplicationforclassifyingdepression

Abstract Depression brings significant challenges to the overall global public health. Each day, millions of people suffered from depression and only a small fraction of them undergo proper treatments. In the past, doctors will diagnose a patient via a face to face session using the diagnostic criteria that determine depression such as the Depression DSM-5 Diagnostic Criteria. However, past research revealed that most patients would not seek help from doctors at the early stage of depression which results in a declination in their mental health condition. On the other hand, many people are using social media platforms to share their feelings on a daily basis. Since then, there have been many studies on using social media to predict mental and physical diseases such as studies about cardiac arrest (Bosley et al., 2013), Zika virus (Miller, Banerjee, Muppalla, Romine, & Sheth, 2017), prescription drug abuse (Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015) mental health (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) and studies particularly about depressive behavior within an individual (Kiang, Anthony, Adrian, Sophie, & Siyue, 2015). This research particularly focuses on leveraging social media data for detecting depressive thoughts among social media users. In essence, this research incorporated text analysis that focuses on drawing insights from written communication in order to conclude whether a tweet is related to depressive thoughts. This research produced a web application that performs a real-time enhanced classification of tweets based on a domain-specific lexicon-based method, which utilizes an improved dictionary that consists of depressive and non-depressive words with their associated orientations to classify depressive tweets. Problem understanding or Business Understanding Depression is the main cause of disability worldwide (De Choudhury et al., 2013). Statistically, an estimation of nearly 300 million people around the world suffers from depression. Shen et al (2017) mentioned that approximately 70% of people with early stages of depression would not consult a clinical psychologist. Many people are utilizing social media sites like Facebook and Instagram to disclose their feelings. This research persists the hypothesis that there are similarities between the mental state of an individual and the sentiment of their tweets and investigated the potentiality of social media (like twitter) as a data source for classifying depression among individuals.

AnApplicationForClassifyingDepression

Research title: "An Application for Classifying depression in tweets" Published in the 2nd International Conference on Computing and Big Data (ICCBD 2019)

Abstract Depression brings significant challenges to the overall global public health. Each day, millions of people suffered from depression and only a small fraction of them undergo proper treatments. In the past, doctors will diagnose a patient via a face to face session using the diagnostic criteria that determine depression such as the Depression DSM-5 Diagnostic Criteria. However, past research revealed that most patients would not seek help from doctors at the early stage of depression which results in a declination in their mental health condition. On the other hand, many people are using social media platforms to share their feelings on a daily basis. Since then, there have been many studies on using social media to predict mental and physical diseases such as studies about cardiac arrest (Bosley et al., 2013), Zika virus (Miller, Banerjee, Muppalla, Romine, & Sheth, 2017), prescription drug abuse (Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015) mental health (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) and studies particularly about depressive behavior within an individual (Kiang, Anthony, Adrian, Sophie, & Siyue, 2015). This research particularly focuses on leveraging social media data for detecting depressive thoughts among social media users. In essence, this research incorporated text analysis that focuses on drawing insights from written communication in order to conclude whether a tweet is related to depressive thoughts. This research produced a web application that performs a real-time enhanced classification of tweets based on a domain-specific lexicon-based method, which utilizes an improved dictionary that consists of depressive and non-depressive words with their associated orientations to classify depressive tweets.

Problem Statement: Depression is the main cause of disability worldwide (De Choudhury et al., 2013). Statistically, an estimation of nearly 300 million people around the world suffers from depression. Shen et al (2017) mentioned that approximately 70% of people with early stages of depression would not consult a clinical psychologist. Many people are utilizing social media sites like Facebook and Instagram to disclose their feelings. This research persists the hypothesis that there are similarities between the mental state of an individual and the sentiment of their tweets and investigated the potentiality of social media (like twitter) as a data source for classifying depression among individuals.