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This research discusses a novel method for premature identification of suicidal thoughts. Although nearby are even now voluminous proven procedures, this one is a merging technique. This study presents the distinctions between LSTM and CNN-LSTM merging, contrasts these distinctions, and determines which one provides higher validation accuracy for the suicide detection of depressed individuals based on their tweets. Having an assertion precision of 96.28% and a validation deduction of 0.1071, the put forward model executes superior than them. Each of the two algorithms had been refined using a single bespoke records, of which half came from optimism140 and the other half was taken straight out of Twitter’s API. Furthermore, this graft elucidates how accuracy can be increased by incorporating a convolutional layer into LSTM.

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Depression Detection on Twitter Using Hybrid CNN-LSTM Model

  • 会议论文

  • 发表日期：

    2025年

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  • M. Suneetha,
  • Nayeema Mohammed

摘要

This research discusses a novel method for premature identification of suicidal thoughts. Although nearby are even now voluminous proven procedures, this one is a merging technique. This study presents the distinctions between LSTM and CNN-LSTM merging, contrasts these distinctions, and determines which one provides higher validation accuracy for the suicide detection of depressed individuals based on their tweets. Having an assertion precision of 96.28% and a validation deduction of 0.1071, the put forward model executes superior than them. Each of the two algorithms had been refined using a single bespoke records, of which half came from optimism140 and the other half was taken straight out of Twitter’s API. Furthermore, this graft elucidates how accuracy can be increased by incorporating a convolutional layer into LSTM.