NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Supporting Online Shopping with Opinion Mining
Abstract
Consumers often find product reviews very valuable.
In online shopping, opinions that are expressed in
product reviews are available in the form of
unstructured text. Existing shopping websites offer
search tools suited to structured product information,
thus customers looking for product opinions are forced
to perform time-consuming analyses manually. This
work proposes a method for seamless integration of
unstructured information available in product reviews
with structured product descriptions using opinion
mining. We demonstrate applicability of our approach
with a used car product search tool using real data.
1. Introduction
Many online shopping decisions are made after
consulting other customers’ opinions. This effect is
especially visible in travel bookings (97,7%) where
77.9% decision involve the use of customer reviews as
a source of information [1]. Consulting reviews requires
significant amount of additional effort from customers.
This work proposes new method for extraction of
valuable product information from customer reviews
and its integration with structured product descriptions.
2. The Method
An opinion mining system needs to fulfill three
generic tasks [2]: identification of the product features,
discovery of opinion phrases, and sentiment analysis. In
our method (see [3] for details), the first of the tasks is
performed using domain knowledge and data from
popular websites offering semi-structured car reviews.
We use a rule-based shallow-parsing method for
extraction of potential opinion statements. The rules are
constructed to extract a consistent fragment of the
sentence that contains a feature and the sentiment about
the feature. Opinion statements are further matched
with lists of opinion words. In comparison to other
approaches our method considers not only nouns as
features and not only adjectives as opinions.
Our approach deals with sentiment analysis on three
levels: word level, chunk level, and context dependant
chunk level. To assess the sentiment we use an
approach similar to [4], where lists of adjectives, nouns,
verbs and adverbs with positive and negative sentiment
were created, combining to the total word sentiment.
Opinion context is modeled with utility theory [5] as the
features were divided in three classes: cost-type - with
preference toward lower values (e.g. price); benefit-type
- higher values are preferred (e.g. reliability); neutral –
the character of a feature is context dependant.
Maciej Dabrowski
Digital Enterprise Research Institute
National University of Ireland Galway, Ireland
maciej.dabrowski@deri.org
98
Figure 1 An example of a used car shopping website
presenting product offers extended with structured
attributes extracted from free-text customer reviews.
The discussed method is implemented in a shopping
website (see Fig. 1) that demonstrates seamless
integration of structured product information (e.g. price)
with unstructured customer opinions.
3. Conclusions
We presented an opinion mining system that extracts
and integrates opinions about products and features
from very informal, noisy text data (product reviews)
using a hierarchy of features from a number of websites
and domain knowledge. Our method is of value not
only to shopping service providers and potential
customers but also to product manufacturers.
4. References
[1] U. Gretzel and K. H. Yoo, "Use and Impact of Online
Travel Reviews " in Information and Communication
Technologies in Tourism Innsbruck, Austria, 2008, pp. 35-46.
[2] A. M. Popescu, B. Nguyen, and O. Etzioni, "OPINE:
extracting product features and opinions from reviews," in
Proceedings of HLT/EMNLP on Interactive Demonstrations,
2005, pp. Association for Computational Linguistics--33.
[3] M. Dabrowski, P. Jarzebowski, T. Acton, and S. O'Riain,
"Improving Customer Decisions Using Product Reviews:
CROM - Car Review Opinion Miner," in 6th International
Conference on Web Information Systems and Technologies
Valencia, Spain: Springer, 2010.
[4] X. Ding, B. Liu, and P. S. Yu, "A holistic lexicon-based
approach to opinion mining," in WSDM '08: Proceedings of
the international conference on Web search and web data
mining, 2008, pp. ACM--240.
[5] J. Butler, D. J. Morrice, and P. W. Mullarkey, "A multiple
attribute utility theory approach to ranking and selection,"
Management Science, vol. 47, pp. 800-816, Jun 2001