# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 6 Conclusions and Future Work  
The integration of deep learning methods into suicide care offers new directions for the improvement of detection of suicide ideation and the possibility for early suicide prevention. Our work takes part in this journey towards the technological improvement in convolutional linguistics to be shared within the research community and successfully implemented in mental health care. In our study, we presented an approach to recognize the existence of suicide ideation signs in Reddit social media and focused on detecting the most effective performance improvement solutions. For such purpose, we built our system on subreddit data corpus created by suicide-indicative and non-suicidal posts. We used different data representation techniques to reformulate the text of the posts into the presentation that our system can recognize. In particular, we characterized a closer connection between the suicidal thoughts and language usage by applying various NLP and text classification techniques. We described the experiment with LSTM-CNN networks built on the top of word2 vec features, and observed the potential of CNN in multiple texts classification tasks. Based on our experiment, the proposed LSTM-CNN hybrid model considerably improves the accuracy of text classification. The main reason the model outperforms other machine learning classifiers is that it combines the strengths of both LSTM and CNN algorithms, and makes up their shortcomings. First, it takes advantage of the LSTM to maintain context information in a long text by keeping the previous tokens and resolves the problem of vanishing gradient. Second, it uses the CNN layer to extract the local pattern using the richer representation of the original input of the text and able to process the text considering not only single words but also their combinations of different predefined sizes trying to learn their best combinations and interpretations. Using this approach, we can ensure that the hybrid model can effectively improve the prediction results as we try to prove in our experiment. Our aim was not to explore the detailed sensitivity of CNN hyper-parameters with respect to the designed decisions. However, we rather tried to improve the potential of CNN neural network classifier for suicide ideation tasks. During our data analysis, we identified the features with the depictions of suicidal tendencies. We observed a considerable shift in the language usage of at-risk individuals. The signs of frustration, hopelessness, negativity or loneliness were significantly detected accompanied by users' preoccupation with themselves. According to our comparative evaluation, we specifically demonstrated the strength and potential of CNN. It resulted in the highest performance among other classification approaches chosen for our experiment, including LSTM as an artificial recurrent neural network. Through the hyper-parameter optimization, we were able to achieve an improvement based on the adaptive hyper-parameter tuning. Although our research findings show that the performances of applied classification approaches are reasonably good, the absolute value of the metrics indicates that this is a challenging task worthy of further study. In our future work, we might try to access a larger dataset with suicide ideation content and a new dataset with related topics. We might examine the correlation between suicidal ideation and family environment, weather, etc. Both datasets will be collected from different social media sources for further demonstration and comparison with our proposed hybrid model. In addition, the performance of the datasets will be applied for further investigation with other deep learning classifiers, such as C-LSTM, RNN and their combined models, accompanied by various parameter optimization evaluations. Limitation of our experiment can be found in its data deficiency and annotation bias. Data deficiency is one of the most critical issues of current research [86], where mainly supervised learning techniques are applied.