# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 2 Related Works  
Current research approaches the task as a text classification problem by utilizing supervised ML algorithms, deep learning (DL) models, and natural language processing techniques. For example, [PERSON] et al. [19] built a ML classifier that differentiates suicidal and non-suicidal forum posts. For the dataset creation, the posts were downloaded from r/SuicideWatch, r/Depression, r/Anxiety, and r/ShowerThoughts subreddits. Then, a subset of posts was annotated under the consultation of two psychiatrists. The features extracted using TF-IDF, LIWC, and sentiment analysis were used to train four classification algorithms: ZeroR, LR, RF, and SVM. The authors of this study also applied ANOVA to reduce the number of TF-IDF features. [PERSON] et al. [20] trained SVM and LR models with TF-IDF and emotion features to detect varying levels of suicide risk. The features were extracted from the UMD Reddit Suicidality Dataset created by [PERSON] et al. [21] for the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych 2019). The posts in this dataset are labeled as no risk, low risk, moderate risk, or severe risk. To address the same multi-level suicide risk detection task, [PERSON] et al. [22] utilized TF-IDF, LIWC, Global Vectors for Word Representation (GloVe), and statistical features. The resulting features were used to train LR, SVM, and RF. This study also applied the chi-square features selection method to tackle high dimensionality. Similarly, [PERSON] et al. [23] focused on addressing high dimensionality in their study, exploring both filter and wrapper feature selection techniques to reduce TF-IDF, \(n\)-gram, and LIWC features. The researchers utilized a dataset by [PERSON] et al. [12] for their analysis. In the study, NB, SVM, RF, and k-nearest neighbors algorithms were trained. In addition to machine learning methods, studies often apply DL models to detect suicidal ideation. These studies frequently implement convolutional neural networks (CNNs) and long short-term memory (LSTM) techniques. However, unlike traditional machine learning, deep learning methods do not require feature engineering. Instead, they use word embeddings generated with GloVe and Word2 Vec. For instance, [PERSON] et al. [12] addressed suicidal ideation detection with both ML and DL methods. However, in this study, both Reddit and Twitter were used as data sources. The Twitter dataset was manually annotated by researchers. The datasets were then used to train ML algorithms such as SVM, RF, Gradient Boost, and XGBoost, as well as two DL models: feed-forward neural network and LSTM. The features for the ML models were extracted with LIWC, latent Dirichlet allocation, TF-IDF, and part-of-speech techniques. The Word2 Vec word embedding technique was applied to create a vector representation of textual data for training DL networks. The study demonstrated the feasibility and practicability of using ML and DL for detecting suicidal ideation and found that the performance improved when models were trained with more features. The dataset created in [12] was also utilized by [PERSON] et al. [15]. Similarly, the authors of this study compared a range of methods consisting of both ML and DL classifiers. TF-IDF, bag-of-words, and statistical characteristics were used as features when training the RF, NB, SVM, and XGBoost, while Word2 Vec embeddings were used with LSTM and CNN. The proposed ensemble comprising LSTM and CNN outperformed the baseline models. The results supported the strength of DL algorithms in suicidal ideation detection. Similarly, the ensemble model was explored in [14]. The study used the UMD Reddit Suicidality Dataset. The authors of this study proposed a LSTM and CNN combined ensemble model to address a multiclass classification problem for detecting different levels of suicidality. The combined LSTM and CNN ensemble model enables the strengths of both methods to be leveraged.