  Suicidal ideation detection from social media is an evolving research with
great challenges. Many of the people who have the tendency to suicide share
their thoughts and opinions through social media platforms. As part of many
researches it is observed that the publicly available posts from social media
contain valuable criteria to effectively detect individuals with suicidal
thoughts. The most difficult part to prevent suicide is to detect and
understand the complex risk factors and warning signs that may lead to suicide.
This can be achieved by identifying the sudden changes in a user behavior
automatically. Natural language processing techniques can be used to collect
behavioral and textual features from social media interactions and these
features can be passed to a specially designed framework to detect anomalies in
human interactions that are indicators of suicidal intentions. We can achieve
fast detection of suicidal ideation using deep learning and/or machine learning
based classification approaches. For such a purpose, we can employ the
combination of LSTM and CNN models to detect such emotions from posts of the
users. In order to improve the accuracy, some approaches like using more data
for training, using attention model to improve the efficiency of existing
models etc. could be done. This paper proposes a LSTM-Attention-CNN combined
model to analyze social media submissions to detect any underlying suicidal
intentions. During evaluations, the proposed model demonstrated an accuracy of
90.3 percent and an F1-score of 92.6 percent, which is greater than the
baseline models.
