Challenges and Solutions in the Opinion Summarization of User-generated Contents
The present is marked by the influence of the Social Web on societies and people worldwide. In this context, users generate large amounts
of data, especially containing opinion, which has been proven useful for many
real-world applications. In order to extract knowledge from the user-generated
content, automatic methods must be developed. In this paper, we present different approaches to summarizing opinion from blogs and reviews. We apply
these approaches to: a) identify positive and negative opinions in blog threads
in order to produce a list of arguments in favor and against a given topic
and b) summarize the opinion expressed in reviews. Subsequently, we evaluate the proposed methods on two distinct datasets and analyze the quality of
the obtained results, as well as discuss the errors produced. Finally, we conclude that the proposed approaches are appropriate in the context of opinion
summarization.
BALAHUR DOBRESCU Alexandra;
KABADJOV Mijail;
STEINBERGER Josef;
STEINBERGER Ralf;
MONTOYO Andrés;
2012-12-04
SPRINGER
JRC67503
0925-9902,
http://www.springerlink.com/content/5750629542266720/,
https://publications.jrc.ec.europa.eu/repository/handle/JRC67503,
10.1007/s10844-011-0194-z,
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