Exploring Sentiment Detection of Social Media Posts with Motion Aware AI: A Research Paper

Authors: Kavita Gadakh, Shrushti Kale, Yogini Pawar, Kirti Kadlag, N.L. Bhale

Country: India

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Abstract: Today, early detection of depression stands as a paramount concern in psychology. Mental health issues, particularly depression, affect a substantial portion of the global population, with over 300 million individuals currently afflicted. Researchers are increasingly exploring the potential of utilizing data from social media platforms to identify mental health issues among users. Depression remains a significant societal challenge, with predicting depressive states still presenting a considerable challenge despite the prevalence of devices like smartphones. Social media analysis often serves as a means to tackle this issue. This article proposes a system for rating depression and detecting suicidal thoughts, aiming to predict suicidal behavior based on the severity of depression. Established classifiers are employed to discern whether an individual is experiencing depression by analyzing their online activity. Machine learning algorithms are utilized to train and classify users into various levels of depression on a scale from 0 to 100%. These algorithms serve as predictive tools for early detection of depression and related mental disorders. The primary contribution of this study lies in examining a network of competencies and their implications for assessing the degree of depression. The system aims to comprehensively understand the model used to categorize users with depression by examining instances where user labels are analyzed to infer post labels. By considering all potential post tag categories, temporary post profiles are generated to classify users with depression. The research demonstrates discernible variations in posting patterns between depressed and non-depressed users, as indicated by the combined likelihood of post tag categories. Natural Language Processing (NLP) techniques, utilizing the BERT framework, are employed to potentially enhance the detection of depression in a more accessible and efficient manner.

Keywords: Machine Learning, NLP, BERT Algorithm, Depression, Classification, Social Media Post

Paper Id: 230588

Published On: 2024-04-23

Published In: Volume 12, Issue 2, March-April 2024

Cite This: Exploring Sentiment Detection of Social Media Posts with Motion Aware AI: A Research Paper - Kavita Gadakh, Shrushti Kale, Yogini Pawar, Kirti Kadlag, N.L. Bhale - IJIRMPS Volume 12, Issue 2, March-April 2024.

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