Abstract:
The internet-based technologies have influenced all parts of human lives within a
short time. The internet is used for conducting commercial transactions electronically
and it is the base of the concept called e-commerce. Most of the businesses have
engaged in utilizing the Internet to sell their product and services. Hence, spend
millions of dollars to create and maintain their corporate websites. The consumer
behaviour in online shopping is continuously changing due to the personal
characteristics of the shoppers as well as the environmental factors. The e-commerce
based transactions are becoming increasingly popular and the number of consumers
who interact with the e-commerce sites have been drastically increased along with
the reviews they leave after purchases. This makes it difficult for potential customers
to read, comprehend, and make sound decisions on individual purchases.
Furthermore, makes even difficult task for the corporate entities to track their
websites to manage customer opinions. Text mining is the process which explores,
evaluates, and interprets data patterns by converting unstructured text data into more
meaningful information. In this study, we address the aforementioned issues by
proposing a Human Computer Interaction (HCI) enabled Naïve Bayes classification
approach to categorize the online reviews of e-commerce websites. HCI factors such
as; usability, simplicity, and accessibility are considered along with consumer
reviews extracted from the attribute dictionaries such as stanford parser. The study
has derived different data patterns from the text mining exercises which will be
beneficial for predictive analytics from the customers’ as well as from the corporate
standpoint for online purchases.
