# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 2 Background and Related Work
[26] studied the adaptation of information retrieval methods for identifying a destructive informational influence in social networks. He built a dictionary of terms pertaining to a suicidal content. He introduced TF-IDF matrices and singular vector decompositions for them. [PERSON] et al. [27] improved the performance of Random Forest (RF) classifier for identification of suicide ideation in tweets. Logistic regression classification algorithms applied in [PERSON] et al. [28] showed promising results in detecting suicidal content with 80-92% accuracy rate. With recent advances of neural network models in natural language processing, a new contribution on detection of suicide ideation has emerged from the implementations of more sophisticated deep learning architectures to outperform more traditional machine learning systems. Recurrent neural network (RNN) is well designed for sequence modeling [29]. In particular, long short-term memory (LSTM) is considered to be one of the effective models able to keep useful information from long-range dependency. [PERSON] et al. [30] work revealed the strength and ability of C-LSTM-based models as compared to other deep learning and machine learning classifiers for suicide ideation recognition. [PERSON] et al. [31] compared the LSTM classifier with five other machine learning models and demonstrated the feasibility and practicability of the approaches. His study provides one of the major benchmarks for the detection of suicide ideation on Reddit SuicideWatch and Twitter. Over the recent past, CNN neural networks with convolutional, nonlinear and pooling layers has been successfully applied to a wide range of NLP tasks and has proven to gain better performance than traditional NLP methods [29]. It, however, emphasizes the local n-gram features and prevents capturing long-range interactions. [PERSON] et al. [32] advocated the strength of CNN on n-gram features from various sentence positions. [PERSON] and [PERSON] [33] introduced a multichannel word embedding and unsupervised pre-training model to improve the classification accuracy. [PERSON] et al. [34] used cTAKES and LR approaches with n-gram features to compare the CNN model to more traditional rule-based entity extraction systems. His findings show CNN to outperform other phenol-typing algorithms on the prediction of 10 phenotypes. [PERSON] et al. [35] showed the strength of CNN and LSTM models for a suicide risk assessment presenting the results for a novelly tested personality and tone features. [PERSON] et al. [36] and [37] highlighted CNN's performance over other approaches to identify the presence of suicidal tendencies among adolescents. [PERSON] et al. [38] applied deep learning methods to detect psychiatric stressors for suicide recognition in social media. Using CNN networks, he built a binary classifier to separate suicidal tweets from non-suicidal tweets. Other recent studies [39] revealed positive results of CNN implementations on SuicideWatch forum which serves as a dataset in our research paper. Fundamentally, single recurrent and convolutional neural networks applied as vectors to encode an entire sequence tend to be insufficient to capture all the important information sequence [40; 41]. As a result, there have been several experiments to develop a hybrid framework for coherent combinations of CNNs and RNNs to apply the merits for both. For instance, [PERSON] et al. [42] introduced a novel neural network model based on a hybrid of ConvNet and BI-LSTMs to solve the measurement problem of a semantic textual similarity. [PERSON] et al. [43] proposed an efficient hybrid model which combines a fast deep model with an initial information retrieval model to effectively and efficiently handle AS. In our study, we propose a framework based on the ensemble of LSTM and CNN combined model to recognize suicide ideation in social media.