Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Konstantina Lazaridou

Hasso-Plattner-Institut für Softwaresystemtechnik

Address: Prof.-Dr.-Helmert-Straße 2-3, D-14482 Potsdam
Phone: +49 331 5509 292
Room: House E, 2 - 02.2.
Email:  konstantina.lazaridou(at)hpi.de

 

Ph.D. candidate at the Infomation Systems Research Group and member of the Web Science Group at the Hasso Plattner Institute for Software Systems Engineering.

 

Research Interests

  • Graph Mining
  • Social Network Analysis
  • Web Data Mining
  • Opinion and Sentiment Analysis
  • Data Stream Mining

Teaching

  • Information Retrieval and Web Search (Lecture, WS 2015/2016)

Classification of German Newspaper Comments

Christian Godde and Konstantina Lazaridou and Ralf Krestel
In Proceedings of the Conference Lernen, Wissen, Daten, Analysen, volume 1670 of CEUR Workshop Proceedings, pages 299-310, 9 2016 CEUR-WS.org.

Abstract:

Online news has gradually become an inherent part of many people’s every day life, with the media enabling a social and interactive consumption of news as well. Readers openly express their perspectives and emotions for a current event by commenting news articles. They also form online communities and interact with each other by replying to other users’ comments. Due to their active and significant role in the diffusion of information, automatically gaining insights of these comments’ content is an interesting task. We are especially interested in finding systematic differences among the user comments from different newspapers. To this end, we propose the following classification task: Given a news comment thread of a particular article, identify the newspaper it comes from. Our corpus consists of six well-known German newspapers and their comments. We propose two experimental settings using SVM classifiers build on comment- and article-based features. We achieve precision of up to 90% for individual newspapers.

BibTeX file

@inproceedings{lwda16,
author = { Christian Godde and Konstantina Lazaridou and Ralf Krestel },
title = { Classification of German Newspaper Comments },
year = { 2016 },
volume = { 1670 },
pages = { 299-310 },
month = { 9 },
abstract = { Online news has gradually become an inherent part of many people’s every day life, with the media enabling a social and interactive consumption of news as well. Readers openly express their perspectives and emotions for a current event by commenting news articles. They also form online communities and interact with each other by replying to other users’ comments. Due to their active and significant role in the diffusion of information, automatically gaining insights of these comments’ content is an interesting task. We are especially interested in finding systematic differences among the user comments from different newspapers. To this end, we propose the following classification task: Given a news comment thread of a particular article, identify the newspaper it comes from. Our corpus consists of six well-known German newspapers and their comments. We propose two experimental settings using SVM classifiers build on comment- and article-based features. We achieve precision of up to 90% for individual newspapers. },
publisher = { CEUR-WS.org },
series = { CEUR Workshop Proceedings },
booktitle = { Proceedings of the Conference Lernen, Wissen, Daten, Analysen },
priority = { 0 }
}

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last change: Wed, 30 Nov 2016 12:01:22 +0100