A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Using Semantic Dependencies to Mine Depressive Symptoms from Consultation Records
IEEE Intelligent Systems
Reviving the Living, Volume 6: Meaning Making in Living Systems
Reviving the Living, Volume 6: Meaning Making in Living Systems
Psychiatric document retrieval using a discourse-aware model
Artificial Intelligence
Metaphor-based meaning excavation
Information Sciences: an International Journal
Using natural language processing to classify suicide notes
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
BI'10 Proceedings of the 2010 international conference on Brain informatics
Using Web-Intelligence for Excavating the Emerging Meaning of Target-Concepts
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Literal and metaphorical sense identification through concrete and abstract context
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Predicting postpartum changes in emotion and behavior via social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Objective: Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. Materials and method: The system implementing the methodology -Pedesis - harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a ''depression lexicon''. The lexicon is used to automatically evaluate the level of depression in texts or whether the text is dealing with depression as a topic. Results: Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p