Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2010

Lex Elisabeth, Granitzer Michael, Juffinger A.

Facet Classification of Blogs: Know-Center at the TREC 2009 Blog Distillation Task

Proceedings of the 18th Text REtrieval Conference, 2010

Konferenz
In this paper, we outline our experiments carried out at the TREC 2009 Blog Distillation Task. Our system is based on a plain text index extracted from the XML feeds of the TREC Blogs08 dataset. This index was used to retrieve candidate blogs for the given topics. The resulting blogs were classified using a Support Vector Machine that was trained on a manually labelled subset of the TREC Blogs08 dataset. Our experiments included three runs on different features: firstly on nouns, secondly on stylometric properties, and thirdly on punctuation statistics. The facet identification based on our approach was successful, although a significant number of candidate blogs were not retrieved at all.
2010

Lex Elisabeth, Granitzer Michael, Juffinger A., Seifert C.

Efficient Cross-Domain Classification of Weblogs

International Journal of Intelligent Computing Research (IJICR), Vol.1, Issue 2, Infonomics Society, 2010

Journal
Text classification is one of the core applicationsin data mining due to the huge amount ofuncategorized textual data available. Training a textclassifier results in a classification model that reflectsthe characteristics of the domain it was learned on.However, if no training data is available, labeled datafrom a related but different domain might be exploitedto perform cross-domain classification. In our work,we aim to accurately classify unlabeled weblogs intocommonly agreed upon newspaper categories usinglabeled data from the news domain. The labeled newsand the unlabeled blog corpus are highly dynamicand hourly growing with a topic drift, so theclassification needs to be efficient. Our approach is toapply a fast novel centroid-based text classificationalgorithm, the Class-Feature-Centroid Classifier(CFC), to perform efficient cross-domainclassification. Experiments showed that thisalgorithm achieves a comparable accuracy thank-Nearest Neighbour (k-NN) and Support VectorMachines (SVM), yet at linear time cost for trainingand classification. We investigate the classifierperformance and generalization ability using aspecial visualization of classifiers. The benefit of ourapproach is that the linear time complexity enables usto efficiently generate an accurate classifier,reflecting the topic drift, several times per day on ahuge dataset.
2010

Lex Elisabeth, Granitzer Michael, Juffinger A., Muhr M.

Stylometric Features for Emotion Level Classification in News Related Blogs

Proceedings of the 9th ACM RIAO Conference , LE CENTRE DE HAUTES ETUDES INTERNATIONALES D'INFORMATIQUE DOCUMENTAIRE, 2010

Konferenz
Breaking news and events are often posted in the blogospherebefore they are published by any media agency. Therefore,the blogosphere is a valuable resource for news-relatedblog analysis. However, it is crucial to first sort out newsunrelatedcontent like personal diaries or advertising blogs.Besides, there are different levels of emotionality or involvementwhich bias the news information to a certain extent.In our work, we evaluate topic-independent stylometric featuresto classify blogs into news versus rest and to assess theemotionality in these blogs. We apply several text classifiersto determine the best performing combination of featuresand algorithms. Our experiments revealed that with simplestyle features, blogs can be classified into news versus restand their emotionality can be assessed with accuracy valuesof almost 80%.
2010

Lex Elisabeth, Granitzer Michael, Juffinger A.

Objectivity Classification in Online Media

21st ACM SIGWEB Conference on Hypertext and Hypermedia (HT2010), ACM, 2010

Konferenz
In this work, we assess objectivity in online news media. Wepropose to use topic independent features and we show ina cross-domain experiment that with standard bag-of-wordmodels, classifiers implicitly learn topics. Our experimentsrevealed that our methodology can be applied across differenttopics with consistent classification performance.
2010

Lex Elisabeth, Granitzer Michael, Juffinger A.

A Comparison of Stylometric and Lexical Features for Web Genre Classification and Emotion Classification in Blogs

IEEE Computer Society: 7th International Workshop on Text-based Information Retrieval in Procceedings of 21th International Conference on Database and Expert Systems Applications (DEXA 10)., IEEE, 2010

Konferenz
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.
2010

Lex Elisabeth, Khan I., Bischof H., Granitzer Michael

Assessing the Quality of Web Content

Proceedings of the ECML/PKDD Discovery Challenge 2010, Online, 2010

Konferenz
2010

Granitzer Michael, Kienreich Wolfgang, Sabol Vedran, Lex Elisabeth

Knowledge Relationship Discovery and Visually Enhanced Access for the Media Domain

Medien-Wissen-Bildung. Explorationen visualisierter und kollaborativer Wissensräume, Innsbruck University Press, 2010

Konferenz
Technological advances and paradigmatic changes in the utilization of the World Wide Web havetransformed the information seeking strategies of media consumers and invalidated traditionalbusiness models of media providers. We discuss relevant aspects of this development and presenta knowledge relationship discovery pipeline to address the requirements of media providers andmedia consumers. We also propose visually enhanced access methods to bridge the gap betweencomplex media services and the information needs of the general public. We conclude that acombination of advanced processing methods and visualizations will enable media providers totake the step from content-centered to service-centered business models and, at the same time,will help media consumers to better satisfy their personal information needs.
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