Article Structure
Abstract
We present disputant relation-based method for classifying news articles on contentious issues.
Introduction
The coverage of contentious issues of a community is an essential function of journalism.
Background and Related Work
Research has been made on sentiment classification in document-level (Turney et al., 2002, Pang et al., 2002, Seki et al.
Argument Frame Comparison
Establishing an appropriate argument frame is important.
Disputant relation-based method
Disputant relation-based method adopts the oppo-nent-based frame for classification.
Evaluation and Discussion
Our evaluation of the method is twofold: first, we evaluate the disputant partitioning results, second, the accuracy of classification.
Conclusion
We study the problem of classifying news articles on contentious issues.
Topics
news articles
- We present disputant relation-based method for classifying news articles on contentious issues.Page 1, “Abstract”
- It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame.Page 1, “Abstract”
- However, news articles are frequently biased and fail to fairly deliver conflicting arguments of the issue.Page 1, “Introduction”
- In this paper, we present disputant relation-based method for classifying news articles on con-Page 1, “Introduction”
- The method helps readers intuitively view the news articles through the opponent-based frame.Page 1, “Introduction”
- However, such frames are often not appropriate for classifying news articles of a contention.Page 1, “Introduction”
- Unlike debate posts or product reviews news articles , in general, do not take a position explicitly (except a few types such as editorials).Page 2, “Introduction”
- As we deal with non-English (Korean) news articles , it is difficult to obtain rich resources and tools, e.g., WordNet, dependency parser, annotated corpus such as MPQA.Page 2, “Introduction”
- The discourse of contentious issues in news articles show different characteristics from that studied in the sentiment classification tasks.Page 2, “Background and Related Work”
- For example, a news article can cast a negative light on a government program simply by covering the increase of deficit caused by it.Page 2, “Background and Related Work”
- News articles of a contentious issue are more diverse than debate articles conveying explicit argument of a specific side.Page 3, “Background and Related Work”
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sentiment classification
- Research on sentiment classification and debate stance recognition takes a topic-oriented view, and attempts to perform classification under the ‘positive vs. negative’ or ‘for vs. against’ frame for the given topic, e. g., positive vs. negative about iPhone.Page 1, “Introduction”
- Research has been made on sentiment classification in document-level (Turney et al., 2002, Pang et al., 2002, Seki et al.Page 2, “Background and Related Work”
- The discourse of contentious issues in news articles show different characteristics from that studied in the sentiment classification tasks.Page 2, “Background and Related Work”
- They assume a debate frame, which is similar to the frame of the sentiment classification task, i.e., for vs. against the debate topic.Page 2, “Background and Related Work”
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SVM
- We applied a modified version of HITS algorithm and an SVM classifier trained with pseudo-relevant data for article analysis.Page 1, “Abstract”
- We applied a modified version of HITS algorithm to identify the key opponents of an issue, and used disputant extraction techniques combined with an SVM classifier for article analysis.Page 2, “Introduction”
- As for the rest of the sentences, a similarity analysis is conducted with an SVM classifier.Page 7, “Disputant relation-based method”
- where SU: number of all sentences of the article Qi: number of quotes from the side i. Qij: number of quotes from either side i or j. Si: number of sentences classified to i by SVM .Page 7, “Disputant relation-based method”
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f-measure
- The performance is measured using precision, recall, and f-measure .Page 8, “Evaluation and Discussion”
- We additionally used the weighted f-measure (wF) to aggregate the f-measure of the three categories.Page 8, “Evaluation and Discussion”
- The overall average of the weighted f-measure among issues was 0.68, 0.59, and 0.48 for the DrC, QbC, and Sim.Page 8, “Evaluation and Discussion”
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Named Entity
- We observe that the method achieves acceptable performance for practical use with basic language resources and tools, i.e., Named Entity Recognizer (Lee et al.Page 2, “Introduction”
- A named entity combined with a topic particle or a subject particle is identified as the subject of these quotes.Page 4, “Disputant relation-based method”
- We detect the name of an organization, person, or country using the Korean Named Entity Recognizer (Lee et al.Page 4, “Disputant relation-based method”
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