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Enhancing suicidal ideation detection through advanced feature selection and stacked deep learning models

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Abstract

Detecting suicidal ideation on communication platforms such as social media is critical for suicide prevention, as these platforms are frequently used for emotional expression and can reflect significant behavior changes. Many machine learning and deep learning techniques have been employed to address this issue, utilizing embedding methods such as Count Vector, Term Frequency-Inverse Document Frequency, Bidirectional Encoder Representations from Transformers, Multilingual Universal Sentence Encoder etc generate high-dimensional vectors. Directly inputting word embeddings into models can introduce noise and outliers, which may negatively impact predictive accuracy. Therefore, feature selection to optimize the dimensionality of word embedding vectors has emerged as a promising direction for future research. This study proposes a feature selection method called Propose Best Feature Selection, which combines Grey Wolf Optimization, Recursive Feature Elimination, and Stepwise Feature Selection. It uses a Voting Classifier to identify and filter the most significant features, reducing dimensionality. These optimized features are then fed into a stacked ensemble hybrid model, with Bi-Directional Gated Recurrent Unit with Attention and Convolutional Neural Network, acting like base and Extreme Gradient Boostis working like the meta-classifier, achieving an accuracy of 98% in Reddit and 97% in Twitter(X) dataset, outperforming similar methods in the field. This work is focused on textual data, and future efforts may expand to include multimodal analysis, incorporating image-based emotional cues. Scalability challenges for large datasets and real-time applications remain a key limitation.

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Data Availability

Data set is available on Kaggale and Github. Kaggle (Reddit Dataset): https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch Github (Twitter Dataset): https://github.com/laxmimerit/twitter-suicidal-intention-dataset

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  1. Maheshwari Prasad Singh contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science Engineering, National Institute of Technology, Patna, Patna, 80005, Bihar, India

    Shiv Shankar Prasad Shukla & Maheshwari Prasad Singh

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  1. Shiv Shankar Prasad Shukla
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Contributions

Shiv Shankar Prasad Shukla and Maheshwari Prasad Singh conceived the idea of using Voting classifier optimization PBFS for optimal feature selection and using an advance embedding method like BERT and hybrid model Classifier to detect behaviour changes from social media posts. The experiments and initial draft were developed by Shiv Shankar Prasad Shukla. Maheshwari Prasad Singh corrected the initial draft. All authors reviewed the manuscript.

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Correspondence to Shiv Shankar Prasad Shukla.

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Shukla, S.S.P., Singh, M.P. Enhancing suicidal ideation detection through advanced feature selection and stacked deep learning models. Appl Intell 55, 303 (2025). https://doi.org/10.1007/s10489-025-06256-0

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