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Exploiting New Sentiment-Based Meta-level Features for Effective Sentiment Analysis
2016
Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16
In this paper we address the problem of automatically learning to classify the sentiment of short messages/reviews by exploiting information derived from meta-level features i.e., features derived primarily from the original bag-of-words representation. We propose new meta-level features especially designed for the sentiment analysis of short messages such as: (i) information derived from the sentiment distribution among the k nearest neighbors of a given short test document x, (ii) the
doi:10.1145/2835776.2835821
dblp:conf/wsdm/CanutoGB16
fatcat:igse7dbpkjebzp2qq56xogawhu