# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
###### Abstract
The studies in the domain that address suicidal ideation detection with machine learning (ML) typically utilize a wide array of methods to generate features, including the bag-of-words, part-of-speech, term frequency-inverse document frequency (TF-IDF), topic modeling with latent Dirichlet allocation, sentiment analysis, linguistic inquiry and word count (LIWC), and various lexicons. The resulting features are then used as input into classification algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision trees (DT), random forest (RF), and XGBoost. Most studies combine different types of features. However, it tends to lead to high dimensionality of the resulting matrix, which can be problematic. We address this concern in our paper by implementing the Boruta feature selection technique to reduce the number of features. In addition to that, the majority of studies primarily focus on achieving higher classification performance, with few of them investigating which features indeed contribute to the effective classification of suicidal texts. This is potentially concerning, because the identification of indicative features can provide crucial insights into suicidal people's thought processes and behaviors. Although some studies perform comparative analyses of suicidal and non-suicidal texts, they are generally limited to \(n\)-gram analysis [14; 15; 16]. In this paper, we investigated which textual and psycholinguistic features were the most contributive to suicidal ideation detection. Our primary aim of this paper is to present a machine learning approach for suicidal ideation detection that combines textual and psycholinguistic features extracted from Reddit data. Firstly, we extracted three types of features using TF-IDF and LIWC techniques and National Research Council (NRC) lexicons. Afterward, we applied the Boruta algorithm to select the most informative features for suicidal ideation detection. A set of four algorithms--LR, NB, SVM, and RF--were trained, and their classification performance was evaluated using 5-fold cross-validation. We also experimented with different combinations of features to determine which one resulted in the higher performance in classifying suicidal texts. The main contributions of this study are as follows:Identification of textual and psycholinguistic features that were the most informative for the classification of suicidal texts using the Boruta algorithm. The application of Boruta in current studies is mainly applied to either personality data or clinical assessments obtained through surveys or questionnaires [17; 18]. * Experimentally determined which combination of textual and psycholinguistic features led to improved classification performance. The article is structured as follows: Section 2 reviews related studies on suicidal ideation detection on Reddit. Section 3 describes the methodology of our proposed approach and experiment design. Section 4 presents the results. Section 5 discusses our findings, and in Section 6, the conclusions of this study are summarized.