Abstract
The advancement in technology and development of various communication platforms transforming the way human being communicate. WWW makes social networks (Facebook, Twitter, Instagram and Snapchat) as a popular platform for people to share their opinion, feelings, and emotions. Sentiment analysis is the process of studying text and extracting out the facts and relevant information from the users opinion. Depressed individuals have different language and they consider social media to share their emotions and views. Social network provides various clues about the person onset of depression like low social interaction and activity, taking medicinal concerns, mainly focus on self and high rate of activity at night. In this field, machine learning performs well but with changing scenario, deep learning is used and provides good results. This survey is a summary of work done in sentiment analysis for depression detection, challenges in sentiment analysis, and various techniques for depression detection.
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Verma, B., Gupta, S., Goel, L. (2021). A Survey on Sentiment Analysis for Depression Detection. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_2
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