E-034 Improving Tweet Classification Accuracy through Automatic Tweaking of Training Set
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概要
- 論文の詳細を見る
Twitter has become a valuable source of information for extracting early symptoms to predict changes in different economic and social indicators. However, misclassification of relevant tweets can easily lead to a 'cry wolf' situation. We have presented a framework to automatically identify noisy tweets in the training set that may confound the judgment of a classifier. We have also modified conventional likelihood based collocation feature selection method. Even with relatively small training set, our method could achieve better classification accuracy.
- FIT(電子情報通信学会・情報処理学会)運営委員会の論文
- 2012-09-04
著者
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Sezaki Kaoru
Institute Of Industrial Science (iis) The University Of Tokyo
-
Iwai Masayuki
Institute Of Industrial Science The University Of Tokyo
-
Khan Muhammad
Institute of Industrial Science, The University of Tokyo
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Sezaki Kaoru
Institute of Industrial Science, The University of Tokyo:Center for Spatial Information Science, The University of Tokyo
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Iwai Masayuki
Institute of Industri al Science University ofTokyo
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