Research on Sentimental Analysis on Mental Health Using Social Media
Research on Sentimental Analysis on Mental Health Using Social Media
This project investigates the use of machine learning and natural language processing techniques in sentiment analysis of mental health discussions on social media platforms. By employing both traditional methods and advanced deep learning models such as LSTM and BERT, the study aims to achieve precise sentiment classification. The insights gained are intended to support mental health advocacy and strategy development. Future work will focus on improving model robustness and incorporating multi-modal data for enhanced sentiment detection
Sentiment analysis; mental health; social media; machine learning; natural language processing; deep learning; LSTM; BERT; emotion detection
Sentiment analysis; mental health; social media; machine learning; natural language processing; deep learning; LSTM; BERT; emotion detection
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
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