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• W2489723804 abstract "This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them into one of three classes: positive, negative or neutral. As part of this work, we contributed a dataset consisting of approximately 10, 000 tweets, each labeled on a five point sentiment scale by three different people. Experiments demonstrate a detection rate between approximately 70% and an average false alarm rate of approximately 18% across all three classes. The developed classifier has been made available for online use." @default.
• W2489723804 created "2016-08-23" @default.
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• W2489723804 date "2016-05-01" @default.
• W2489723804 modified "2023-09-23" @default.
• W2489723804 title "Sentiment classification of tweets using hierarchical classification" @default.
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• W2489723804 doi "https://doi.org/10.1109/icc.2016.7511391" @default.
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