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Aspect-Based Opinion Mining from Product Reviews Using Conditional Random Fields
Samha, A.K., Li, Y. and Zhang, J.
Product reviews are the foremost source of information
for customers and manufacturers to help them make
appropriate purchasing and production decisions. Natural
language data is typically very sparse; the most common
words are those that do not carry a lot of semantic
content, and occurrences of any particular content-bearing
word are rare, while co-occurrences of these words are
rarer. Mining product aspects, along with corresponding
opinions, is essential for Aspect-Based Opinion Mining
(ABOM) as a result of the e-commerce revolution.
Therefore, the need for automatic mining of reviews has
reached a peak. In this work, we deal with ABOM as
sequence labelling problem and propose a supervised
extraction method to identify product aspects and
corresponding opinions. We use Conditional Random
Fields (CRFs) to solve the extraction problem and
propose a feature function to enhance accuracy. The
proposed method is evaluated using two different
datasets. We also evaluate the effectiveness of feature
function and the optimisation through multiple
experiments. |
Cite as: Samha, A.K., Li, Y. and Zhang, J. (2015). Aspect-Based Opinion Mining from Product Reviews Using Conditional Random Fields. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 119-128 |
(from crpit.com)
(local if available)
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