Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.

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Abstract

Sentiment analysis is an emerging trend nowadays to understand people’s sentiments in multiple situations in their quotidian life. Social media data would be utilized for the entire process ie the analysis and classification processes and it consists of text data and emoticons, emojis, etc. Many experiments were conducted in the antecedent studies utilizing Binary and Ternary Classification whereas Multi-class Classification gives more precise and precise Classification. In Multi-class Classification, the data would be divided into multiple sub-classes predicated on the polarities. Machine Learning and Deep Learning Techniques would be utilized for the classification process. Utilizing Social media, sentiment levels can be monitored or analysed. This paper shows a review of the sentiment analysis on Social media data for apprehensiveness or dejection detection utilizing various artificial intelligence techniques. In the survey, it was optically canvassed that social media data which consists of texts,emoticons and emojis were utilized for the sentiment identification utilizing various artificial intelligence techniques. Multi Class Classification with Deep Learning Algorithm shows higher precision value during the sentiment analysis.© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.