# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 4 Methodology  
The purpose of the present study is to implement a combined deep learning classifier to improve a performance of a language modeling and text classification for detecting suicide ideation in Reddit social media. In our experiment, we incorporate a technical description of approaches using various NLP and text to classify techniques.  
Figure 1 shows a general overview of our proposed framework. It consists of two directions for text data mining methods. The first one consists of data pre-processing, features extraction with NLP techniques (TF-IDF, BOW and Statistical Features) employed to encode the words to be further proceeded by traditional machine learning systems for the baseline methods. The second framework is created by data pre-processing, features extraction using word embedding, followed by deep learning classifiers, one for the baseline method and one for the proposed model.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline \multicolumn{1}{c}{**Suicidal Posts**} & \multicolumn{1}{c}{**Non-Suicidal Posts**} \\ \hline Want to die, end it now, I wanna die with a blunt. I’m going to kill myself this weekend. & There is no one “correct” way to talk to someone struggling with suicidal thoughts, Children of suicide parents. \\ \hline I want to slit my wrists tonight, I tried to commit suicide. & I think you should tell people how you feel, I think suicide is a permanent option that most of the time results out of a temporary issue. \\ \hline I wish guns for suicide, nobody cares if I die. I want to die Where can i go to commit Suicide?? & Will you ever get over the news that one of your parents committed suicide? \\ \hline Where can i go to commit suicide?? & Friend has given up, seriously considering suicide. \\ I don’t know what else to do. & \\ \hline Just over life, die alone, sleep forever. I’m writing my suicide note right now. I plan to kill myself soon. & National Suicide Prevention online chat. I think suicide is a permanent option that most of the time results out of a temporary issue. \\ \hline What[PERSON] the point in living when I will always be alone. & Method used in [PERSON] and [PERSON]’s suicides. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of Human Annotations of Reddit Posts.  
[PERSON] Pre-Processing  
Pre-processing involves filtering of an input text to improve the accuracy of a proposed method by eliminating redundant features to process raw posts prior to the learning of word embedding. It is achieved by applying a series of filters on Reddit posts to transform raw data into a format understood by learning models. In our study, we employ the Natural language toolkit (NLTK) [44] to pre-process the dataset before it proceeds to its training stage. First, we start with a concatenation of the post titles and bodies. We remove duplicated sentences from the original dataset. Next, we use tokenization as a part of the data filtering and a converting process to divide the Reddit posts into individual tokens. Then, we replace all the URL addresses, contractions and redundant white spaces with a single whitespace. We remove brackets, dashes, colons, stop words and all newline symbols, which could lead to erratic results if stayed ignored. In this way, the posts become lowercased and saved as separate text files. We apply lemmatization to ensure that the word endings will not be roughly dropped, which could lead to creating senseless word pieces such as stemming. We rather transform them into word lemmas related to the dictionary. Finally, the cleaned data is ready for word embedding.