ABSTRACT:
Using online consumer reviews as electronic word of
mouth to assist purchase-decision making has become increasingly popular. The
Web provides an extensive source of consumer reviews, but one can hardly read
all reviews to obtain a fair evaluation of a product or service. A text
processing framework that can summarize reviews, would
therefore be desirable. A subtask to be performed by such a framework would be
to find the general aspect categories addressed in review sentences, for which
this paper presents two methods. In contrast to most existing approaches, the
first method presented is an unsupervised method that applies association rule
mining on co-occurrence frequency data obtained from a corpus to find these
aspect categories. While not on par with state-of-the-art supervised methods,
the proposed unsupervised method performs better than several simple baselines,
a similar but supervised method, and a supervised baseline, with an F1-score of 67%. The second method
is a supervised variant that outperforms existing methods with an F1-score of 84%.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor :
15’’ LED
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Input
Devices : Keyboard, Mouse
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Ram : 1
GB
SOFTWARE
REQUIREMENTS:
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Operating
system : Windows 7.
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Coding
Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database
: MYSQL
REFERENCE:
Kim Schouten, Onne van der Weijde, Flavius Frasincar, and Rommert Dekker,
“Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis
With Co-Occurrence Data”, IEEE TRANSACTIONS ON CYBERNETICS, 2018.