ABSTRACT:
Aspect-based opinion mining is
finding elaborate opinions towards a subject such as a product or an event.
With explosive growth of opinionated texts on the Web, mining aspect-level
opinions has become a promising means for online public opinion analysis. In
particular, the boom of various types of online media provides diverse yet
complementary information, bringing unprecedented opportunities for cross media
aspect-opinion mining. Along this line, we propose CAMEL, a novel topic model
for complementary aspect-based opinion mining across asymmetric collections.
CAMEL gains information complementarity by modeling both common and specific aspects across
collections, while keeping all the corresponding opinions for contrastive
study. An auto-labeling scheme called AME is also
proposed to help discriminate between aspect and opinion words without
elaborative human labeling, which is further enhanced
by adding word embedding-based similarity as a new feature. Moreover, CAMEL-DP,
a nonparametric alternative to CAMEL is also proposed based on coupled Dirichlet Processes. Extensive experiments on real-world
multi-collection reviews data demonstrate the superiority of our methods to
competitive baselines. This is particularly true when the information shared by
different collections becomes seriously fragmented. Finally, a case study on
the public event “2014 Shanghai Stampede” demonstrates the practical value of
CAMEL for real-world applications.
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:
Yuan Zuo, Junjie Wu, Hui Zhang, Deqing Wang, Ke Xu, “Complementary
Aspect-based Opinion Mining”, IEEE Transactions on Knowledge and Data
Engineering, 2018.