Libertas is pleased to announce the forthcoming publication of a Biomedical Informatics Insights Supplement on the topic of finding emotions in suicide notes with machine learning tools.

The Supplement will be published next week and will be available without barriers to read through the Biomedical Informatics Insights homepage.

This is the first supplement for Biomedical Informatics Insights and will contain the following papers and an introductory editorial by the Biomedical Informatics Insights editor in chief Dr John P. Pestian

• A Combined Approach to Emotion Detection in Suicide Notes

• A Hybrid Approach to Sentiment Sentence Classification in Suicide Notes

• A Hybrid Model for Automatic Emotion Recognition in Suicide Notes

• A Hybrid system for Emotion Extraction from Suicide Notes

• A Naïve Bayes Approach to Classifying Topics in Suicide Notes

• Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes

• Combining Lexico-semantic Features for Emotion Classification in Suicide Notes

• Discovering Fine-grained Sentiment in Suicide Notes

• Early fusion of low level features for emotion mining

• Emotion Detection in Suicide Notes using Maximum Entropy Classification

• Fine-grained emotion detection in suicide notes: A thresholding approach to multi-label classification

• Labeling Emotions in Suicide Notes: Cost-Sensitive Learning with Heterogeneous Features

• LASSA: Emotion Detection via Information Fusion

• Leveraging Psycholinguistic Resources and Emotional Sequence Models for Suicide Note Emotion Annotation

• Rule-based and Lightly Supervised Methods to Predict Emotions in Suicide Notes

• Sentiment Analysis of Suicide Notes: A Shared Task

• Statistical and similarity methods for classifying emotion in suicide notes

• Suicide Note Sentiment Classification: A Supervised Approach Augmented by Web Data

• Three hybrid classifiers for the detection of emotions in suicide notes

• Topic Categorisation of Statements in Suicide Notes with Integrated Rules and Machine Learning

• Using Ensemble Models to Classify the Sentiment Expressed in Suicide Notes

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