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- W4386804905 abstract "Today, it's common to use depression detection to understand how people are feeling in different situations in their daily lives. Social media data, including text data, emoticons, emojis, and other symbols, is used throughout the process, including the analysis and classification phases. In previous studies, binomial and trinomial classifications were widely used, but multiclass classification provides more accurate analysis. In multiclass classification, polarity is used to separate data into various subclasses. Deep learning and machine learning techniques are used in the categorization process. Depression levels can be guided or analyzed through social media. The purpose of this research is to present an overview of depression identification in social media data using various artificial intelligence algorithms to detect anxiety and sadness. Text, emoticons, and emojis from social media were visually recorded and used for sentiment identification using a variety of artificial intelligence approaches in this study. In sentiment analysis, multiclass categorization utilizing deep learning algorithms yields higher efficiency ratings." @default.
- W4386804905 created "2023-09-17" @default.
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- W4386804905 date "2023-01-01" @default.
- W4386804905 modified "2023-10-16" @default.
- W4386804905 title "Review on Depression Detection on Social Media Using Machine Learning" @default.
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- W4386804905 doi "https://doi.org/10.1007/978-981-99-3716-5_8" @default.
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