In the blogosphere, the amount of digital content is expanding and for search engines, new challenges have been imposed. Due to the changing information need, automatic methods are needed to support blog search users to filter information by different facets. In our work, we aim to support blog search with genre and facet information. Since we focus on the news genre, our approach is to classify blogs into news versus rest. Also, we assess the emotionality facet in news related blogs to enable users to identify people's feelings towards specific events. Our approach is to evaluate the performance of text classifiers with lexical and stylometric features to determine the best performing combination for our tasks. Our experiments on a subset of the TREC Blogs08 dataset reveal that classifiers trained on lexical features perform consistently better than classifiers trained on the best stylometric features.
|Publikationsstatus||Veröffentlicht - 1 Jan 2010|
|Veranstaltung||7th International Workshop on Text-Based Information Retrieval, TIR 2010 - In Conjunction with DEXA 2010 - Bilbao, Spanien|
Dauer: 30 Aug 2010 → 3 Sep 2010
|Konferenz||7th International Workshop on Text-Based Information Retrieval, TIR 2010 - In Conjunction with DEXA 2010|
|Zeitraum||30/08/10 → 3/09/10|
ASJC Scopus subject areas
- Information systems