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Social Media Mining to Detect Mental Health Disorders Using Machine Learning

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Sentiment Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1432))

Abstract

The growth of social media has caused people to post a significant content online. They find it easier to communicate in the virtual world rather than to talk to a person and share their problems. This has given rise to a pertinent question—can we use social media to help these youngsters to understand the state of their mental health? If so, how can we do it without manual intervention so that they do not feel uncomfortable to share their problems? The main purpose of this research was to find out a way in which we can detect mental health disorders without manual intervention. Machine learning can be the right solution to this problem. We used social media sites to analyse the mental state of the users. Machine learning models are trained to analyse the mental health of the people. Machine learning algorithms are employed to analyse the mental health disorders based on social media posts. Sentiment analysis can be used to detect the tone of the user from the message. This tone is useful in identifying whether the tweets are positive or negative. The proposed system uses the popular social media platform—Twitter to detect depression in the users. The tweets collected from Twitter users are used to train the machine learning model to detect whether the tweet is positive or negative. We have used logistic regression to train the model. This results in a model that is able to accurately predict whether the Twitter user is depressed or not. This can be used to detect the depression in the users and link to health workers or helplines which help the depressed person cope with their mental illness.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Saveetha Engineering College, Chennai, 602105, India

    Udaipurwala Rashida & K. Suresh Kumar

Authors
  1. Udaipurwala Rashida
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  2. K. Suresh Kumar
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Correspondence to Udaipurwala Rashida .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. University of West Attica, Greece, Greece

    Klimis Ntalianis

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rashida, U., Suresh Kumar, K. (2023). Social Media Mining to Detect Mental Health Disorders Using Machine Learning. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_68

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