  A new opinion extraction method is proposed to summarize unstructured,
user-generated content (i.e., online customer reviews) in the fixed topic
domains. To differentiate the current approach from other opinion extraction
approaches, which are often exposed to a sparsity problem and lack of sentiment
scores, a confirmatory aspect-based opinion mining framework is introduced
along with its practical algorithm called DiSSBUS. In this procedure, 1) each
customer review is disintegrated into a set of clauses; 2) each clause is
summarized to bi-terms-a topic word and an evaluation word-using a
part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified
topic relevant to a specific domain. The proposed processes have two primary
advantages over existing methods: 1) they can decompose a single review into a
set of bi-terms related to pre-specified topics in the domain of interest and,
therefore, 2) allow identification of the reviewer's opinions on the topics via
evaluation words within the set of bi-terms. The proposed aspect-based opinion
mining is applied to customer reviews of restaurants in Hawaii obtained from
TripAdvisor, and the empirical findings validate the effectiveness of the
method.
  Keywords: Clause-based sentiment analysis, Customer review, Opinion mining,
Topic modeling, User-generate-contents.
