Abstract
The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate speech, inflammatory remarks, and abusive language. The exponential growth of social media has facilitated the widespread circulation of hostile and toxic content, giving rise to an unprecedented influx of hate speech, incendiary language, and abusive rhetoric. The study utilized text representation and word embedding techniques such as bi-gram, tri-gram and FastText that aim to capture the semantic and syntactic information of the text data. Machine learning and deep learning techniques such as CNN, BERT, and SVM have been utilized to classify social media posts into depression and non-depression categories. To assess the effectiveness of the suggested approaches, this work employed performance metrics, including accuracy, precision, recall, and F1-score. The outcomes of the investigation indicate that the SVM can identify symptoms of depression with an average accuracy rate of 80.
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Subramanian, M., Raju, G., Sureshkumar, A., Anbarasu, C., Vadivel, K.S., Nandhini, P.S. (2024). From Words to Emotions: Identifying Depression Through Social Media Insights. In: Chakravarthi, B.R., et al. Speech and Language Technologies for Low-Resource Languages. SPELLL 2023. Communications in Computer and Information Science, vol 2046. Springer, Cham. https://doi.org/10.1007/978-3-031-58495-4_20
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