A Cost Adjusting Method for Increasing Customers’ Sentiment Classification Performance
Keywords:
Sentiment classification, class imbalance problems, social media, text miningAbstract
The internet could be a perfect platform for
spreading the electronic word of mouth (e-WOM). Consumers
not only heavily depended comments regarding the products or
services in social media to make their purchase decisions. The
negative product reviews could cause a negative impact on
business products. When online reviews increase, inevitably
there will produce imbalanced class data, in which the amount
of positive comments (negative comments) is far larger than the
number of negative comments (positive comments). When
training a classifier using this kind of imbalanced data, it’ll lead
to a higher accuracy for determining the majority example, but
an unacceptable error for classifying the minority examples.
However, in the domain of sentiment classification, the
available works didn’t discuss this issue to solve the imbalanced
comments. Therefore, this study aims to find the best
combination from the cost adjustment, under-sampling, and
over-sampling methods based on support vector machines
(support vector machines, SVM) to improve the classification
performance of imbalanced semantic comments. A comparative
analysis of the experimental results will be provided to
evaluation these methods. In addition, we use a real online
travel site reviews as the case study to verify the effectiveness of
the methods.
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