An Unsupervised Machine Learning Model for Identifying Suicidal Posts Using Latent Dirichlet Allocation and Text Feature Extraction
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Abstract
This study aims to enhance the performance of unsupervised machine learning models in identifying suicidal behavior from English-language Reddit posts. Using Python, a model was developed and trained on 232,074 posts with Latent Dirichlet Allocation (LDA) for topic modeling. Text feature extraction techniques incorporating unigram to 4-gram models were applied prior to model input. Four experimental setups tested different feature word sizes: the top 50,000, 100,000, 150,000 most frequent words, and all available features. Results showed that using the top 50,000 features yielded the highest accuracy (85.45%) but the lowest recall (95.55%), while using all features achieved lower accuracy (79.54%) but higher recall (97.70%). The findings suggest that reducing feature quantity enhances model accuracy and focus on suicide-related content, but may compromise recall due to loss of contextual information. Overall, the study concludes that LDA-based models can perform effectively without relying on the full vocabulary of large datasets, though a trade-off between accuracy and recall must be considered.
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References
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