| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 383316 | Expert Systems with Applications | 2012 | 9 Pages |
The use of the social web has brought a series of changes in the way how content is created. In particular, social news sites link stories and the different users can comment them. In this paper, we propose a new method based on different features extracted from the text able to categorise the comments. To this end, we use a combination of statistical, syntactic and opinion features and machine-learning classifiers to classify a comment within three different categorisation types: the focus of the comment, the type of information contained in the comment and the controversy level of the comment. We validate our approach with data from ‘Menéame’, a popular Spanish social news site.
► We propose a new method for representing comments in social news websites. ► We describel this machine-learning-based method for categorising comments in social news sites. ► The method can achieve high accuracy rates in three different classification tasks.
