Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning
The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention. However, the complexity of the natural language constructs makes this task very challenging. Deep Learning architectures such as LSTMs, CNNs, and RNNs show promise in sentence level classification problems. This work investigates the ability of deep learning architectures to build an accurate and robust model for suicidal ideation detection and compares their performance with standard baselines in text classification problems. The experimental results reveal the merit in C-LSTM based models as compared to other deep learning and machine learning based classification models for suicidal ideation detection.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDeep LearningGeneral ClassificationSentenceSentence Classificationtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods
Social media platforms have transformed traditional communication methods by allowing users worldwide to communicate instantly, openly, and frequently. People use social media to express their opinion and share their per…
BIG-bench Machine Learningtext-classificationText ClassificationBuilding and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression…
Deep Learning Based Approach For Detecting Suicidal Ideation in Hindi-English Code-Mixed Text: Baseline and Corpus
Suicide rates are rising among the youth, and the high association with suicidal ideation expression on social media necessitates further research into models for detecting suicidal ideation in text, such as tweets, to e…
A self attention TCN based model for suicidal ideation detection from social media posts
Early suicidal ideation detection has long been regarded as an important task that can benefit both society and individuals. In this regard, it has been shown that, very frequently, the first symptoms of this problem can…
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention
Early intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to detect suicidal ideation.…
Word Embeddings