Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Parsimonious language models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Generating focused topic-specific sentiment lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Adding semantics to microblog posts
Proceedings of the fifth ACM international conference on Web search and data mining
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Online reputation management is about monitoring and handling the public image of entities (such as companies) on the Web. An important task in this area is identifying "aspects" of the entity of interest (such as products, services, competitors, key people, etc.) given a stream of microblog posts referring to the entity. In this paper we compare different IR techniques and opinion target identification methods for automatically identifying aspects and find that (i) simple statistical methods such as TF.IDF are a strong baseline for the task, significantly outperforming opinion-oriented methods, and (ii) only considering terms tagged as nouns improves the results for all the methods analyzed.