Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Multi-domain sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Investigating statistical techniques for sentence-level event classification
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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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Negative life events, such as death of a family member, argument with a spouse and loss of a job, play an important role in triggering depressive episodes. Therefore, it is worth to develop psychiatric services that can automatically identify such events. In this paper, we propose the use of association language patterns, i.e., meaningful combinations of words (e.g., ), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work). The language patterns are discovered using a data mining algorithm, called association pattern mining, by incrementally associating frequently co-occurred words in the sentences annotated with negative life events. The discovered patterns are then combined with single words to train classifiers. Experimental results show that association language patterns are significant features, thus yielding better performance than the baseline system using single words alone.