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HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016)
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- Date
- 1. 2.2016 18:25:22
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Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013)
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- Abstract
- Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410
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Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013)
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- Date
- 23. 3.2013 13:22:36
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Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015)
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- Date
- 4. 8.2015 19:22:04
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Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1668-1678
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Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013)
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- Date
- 28.10.2013 19:22:57
-
Wartena, C.; Sommer, M.: Automatic classification of scientific records using the German Subject Heading Authority File (SWD) (2012)
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- Abstract
- The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.
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Salles, T.; Rocha, L.; Gonçalves, M.A.; Almeida, J.M.; Mourão, F.; Meira Jr., W.; Viegas, F.: ¬A quantitative analysis of the temporal effects on automatic text classification (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1639-1667
