Opinion Extraction, Summarization and Tracking
in News and Blog Corpora
(2006)Abstract
Humans like to express their opinions and are eager to know
others’ opinions. Automatically mining and organizing
opinions from heterogeneous information sources are very
useful for individuals, organizations and even governments.
Opinion extraction, opinion summarization and opinion
tracking are three important techniques for understanding
opinions. Opinion extraction mines opinions at word,
sentence and document levels from articles. Opinion
summarization summarizes opinions of articles by telling
sentiment polarities, degree and the correlated events. In
this paper, both news and web blog articles are investigated.
TREC, NTCIR and articles collected from web blogs serve
as the information sources for opinion extraction.
Documents related to the issue of animal cloning are
selected as the experimental materials. Algorithms for
opinion extraction at word, sentence and document level are
proposed. The issue of relevant sentence selection is
discussed, and then topical and opinionated information are
summarized. Opinion summarizations are visualized by
representative sentences. Text-based summaries in different
languages, and from different sources, are compared.
Finally, an opinionated curve showing supportive and nonsupportive
degree along the timeline is illustrated by an
opinion tracking system.
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