Perera M.R.R. : Suicidal Ideation detection through textual content
Data set Link [](https://drive.google.com/file/d/1mcAoer2-djjDVqwaMyQLrA6YtZcuikCh/view?usp=share_link)
We try to extract valuable information for suicide diagnosis from the user-posted text data. We train model using Convolution Neural Network (CNN) and long short-term memory (LSTM) to recognize text postings mentioning suicide and perform the optimum preprocessing step.
Text posts on social media in text format about suicide refer to people who are suicided and occasionally express their emotions in these posts. We will be able to observe phrases or words that fall into these categories in these text posts.
We are attempting to conclude that LSTM outperforms other machine learning classifiers. We hope that by combining LSTM and CNNs, we will be able to develop a hybrid model that can more accurately identify depression, benefiting people all over the world. We hope that by combining LSTM and CNNs, we will be able to develop a hybrid model that can more accurately identify c, benefiting people all over the world.
This research proposes a smart and context-aware deep learning framework based on CNN to effectively identify mental-health-related problems from social media user posts with improved classification accuracy. This study combines various sources of data for an effective analysis of suicide-related data. We used a knowledge distillation scheme to transfer knowledge from a large pretrained CNN to a smaller model, and we examined suicide-related data using LSTM. The results show that our proposed system accurately handles mixed data and improves the performance of mental health classification
(i) A new framework is presented to extract a huge size of highly appropriate suicide-related data from Reddit. In addition, we implemented a combined cyber-community-group-based labeling and keyword-based data crawling technique based on the circumplex model of emotion to identify the desired mental health problem data.
(ii) A deep neural network-based bidirectional text representation model, that is, CNN, is used to embody mental health problem textual data maintaining contextual and semantic connotations. In addition, we proposed a sequence processing model called long short-term memory (LSTM) as a classifier, which effectively maximizes the amount of information accessible to the network, improving the content available to the algorithm in knowing what words immediately follow and come before a given word in a sentence.
(iii) We propose a knowledge distillation technique, which is a means of transferring knowledge from a large pretrained model (CNN) to a smaller model to maximize performance and accuracy. We filter the large network (CNN) into another much smaller network for mental health-related problem identification, and it performs very well by transferring the required domain knowledge and applying it to a specific healthcare environment.
(iv) We conducted extensive experiments using a principal component analysis (PCA) and different deep learning/ML models, the results of which are compare with other related models. This evaluation plays a key role in regulating the shortcomings of the already applied methods and classification models. The experimental results show that our model performs considerably well over the compare methods, which, after many hyperparameter optimizations, provides a proper accuracy and we can make decision mental health situation of the person
