Sagarlatake/Sentiment-Depression--Analysis-Using-Machine-Learning-Decision-Tree-Naive-Bayes-RF-for-Tweets
Depression Analysis using tweets. Contains implementation notebook, with detailed analysis for depression prediction.
This tool helps mental health researchers, public health analysts, or social scientists analyze Twitter data to identify potential indicators of depression. You input a collection of tweets, and it outputs predictions on which tweets suggest depression, helping you understand sentiment trends related to mental well-being in large datasets.
No commits in the last 6 months.
Use this if you need to quickly assess sentiment related to depression within large volumes of social media text, specifically tweets.
Not ideal if you need a clinical diagnostic tool or a comprehensive mental health assessment beyond social media sentiment.
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Jupyter Notebook
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Last pushed
Oct 03, 2021
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