Sentiment Analysis for Mental Health Using NLP & ML
Classify mental health statements using NLP and ML techniques. Includes preprocessing, TF-IDF, XGBoost, Random Over-Sampling, and real-world prediction applications.
$15 USD
$3.00 USD
Project Outcomes
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A sentiment analysis system helps businesses improve their product offerings by learning what works and what doesn't.
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Marketers can analyze comments on online review sites, survey responses, and social media posts to gain deeper insights into specific product features.
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Achieved accurate classification of mental health statements.
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Preprocessed text using cleaning, tokenization, and stemming.
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Discovered features based on TF-IDF and quantized characteristics.
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Used Random Over-Sampling which means that this dataset is balanced.
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Optimized models such as XGBoost, Logistic Regression, etc were also trained.
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Based on the metrics and analysis, we have also evaluated the models.
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We have been able to identify XGBoost as the best-performing model among the four models.
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Generated insights using WordClouds and data analysis.
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Developed an efficient Framework for text classification tasks.