DEPRESSION DETECTION AND SENTIMENTS ANALYSIS

DEPRESSION DETECTION AND SENTIMENTS ANALYSIS

Manya Srivastava¹, Ichchha Singh², Er.Shilpi Khanna³, Dr. Ajay Kumar Srivastava⁴

¹UG student of Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India

²1UG student of Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India

³Professor, Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India

⁴Professor, Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India

Abstract

Depression has been recognized as a significant health concern worldwide. Depression is one of the most common and disabling mental disorders, and has a relevant impact on society. According to the World Health Organization (WHO), more than 300 million people suffer from depression in their daily lives. Various researchers have shown that features integrating acoustic, textual and visual biomarkers to analyze psychological distress have shown great performances for depression detection. Sentiment analysis is a hot topic that’s been on research for decades. Sentiment analysis (SA) represents a computational study of opinions, sentiments, emotions, and attitudes expressed in texts or social media about a specific topic. Mental health, especially, has become a growing concern due to employment terminations, income loss, family stress and other uncertainties. Nowadays risk of early death is increasing due to mental illness which is mostly caused due to depression. Depression creates suicidal thoughts causing serious impairments in daily life. Sentiment analysis is a hot topic that’s been on research for decades, which intends to find the nature of text and classifies into positive, negative and neutral. In today’s digital world lot of data can be made available for sentiment analysis.

Keywords: Depression, Sentiment Analysis, Social media, Mental health