Automatic detection of text genre
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Exploiting subjectivity classification to improve information extraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
For a few dollars less: identifying review pages sans human labels
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
| Hi-index | 0.00 |
Many online news/blog aggregators like Google, Yahoo and MSN allow users to browse/search many hundreds of news sources. This results in dozens, often hundreds, of stories about the same event. While the news aggregators cluster these stories, allowing the user to efficiently scan the major news items at any given time, they do not currently allow alternative browsing mechanisms within the clusters. Furthermore, their intra-cluster ranking mechanisms are often based on a notion of authority/popularity of the source. In many cases, this leads to the classic power law phenomenon -- the popular stories/sources are the ones that are already popular/authoritative, thus reinforcing one dominant viewpoint. Ideally, these aggregators would exploit the availability of the tremendous number of sources to identify the various dominant threads or viewpoints about a story and highlight these threads for the users. This paper presents an initial limited approach to such an interface: it classifies articles into two categories: fact and opinion. We show that the combination of (i) a classifier trained on a small (140K) training set of editorials/reports and (ii) an interactive user interface that ameliorates classification errors by re-ordering the presentation can be effective in highlighting different underlying viewpoints in a story-cluster. We briefly discuss the classifier used here, the training set and the UI and report on some initial anecdotal user feedback and evaluation.