Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Information Retrieval
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking sentiment in mail: how genders differ on emotional axes
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
From once upon a time to happily ever after: Tracking emotions in mail and books
Decision Support Systems
Portable features for classifying emotional text
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Detecting distressed and non-distressed affect states in short forum texts
LSM '12 Proceedings of the Second Workshop on Language in Social Media
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Historically, suicide risk assessment has relied on question-and-answer type tools. These tools, built on psychometric advances, are widely used because of availability. Yet there is no known tool based on biologic and cognitive evidence. This absence often cause a vexing clinical problem for clinicians who question the value of the result as time passes. The purpose of this paper is to describe one experiment in a series of experiments to develop a tool that combines Biological Markers (Bm) with Thought Markers (Tm), and use machine learning to compute a real-time index for assessing the likelihood repeated suicide attempt in the next six-months. For this study we focus using unsupervised machine learning to distinguish between actual suicide notes and newsgroups. This is important because it gives us insight into how well these methods discriminate between real notes and general conversation.