| dc.description.abstract | With the advancement of technology, customers or consumers can publish or
share the pros and cons of the product or service through a variety of different
channels; when negative customer opinions or evaluations appear, many natives will
follow to respond, and sometimes issues will also be discussed. Because of this, ripple
effect is caused and attention of the masses is attracted. These negative comments can
be called guest complaints. At present, the service companies deal with so called
customer complaints on social platforms, and most of them use customer service
center personnel to manually obtain customer complaints and further process them,
which often slows down in timeliness. The messages of customer complaints usually
also have a high degree of useful information. These customer complaints usually
contain dissatisfactions and hopes for improvements, which is very important for the
organization.
Data sets used in this research are gathered from user reviews between January 1,
2014 and April 30th, 2020 on Google Play platform, 31,401 data sets in total. A In
this article, customer complaints analyzation and problem category prediction are
accomplished based on Supervised Machine Learning Methods, for instance, Naive
Bayesian Calculations. After feature words extracted from unstructured user
complaints and analyzed with Orange exploration tools, a keyword vocabulary was
built, modelled and labelled, which includes six main dimensions.This research shows
that Multi-Class Classification has higher prediction accuracy on compound keyword
database, comparing with Binary Classification, which has higher accuracy when
applied on keyword database with single transitive verbs. It is also proved that
customer complaints could be efficiently classified and saved time from manual
classifications.
Keywords: Text Mining, classification prediction, supervised learning | en_US |