With the enormous platforms available in present days, consumers
communicate and interconnect online with web users all around the world
to share their experiences. Thus, online platform has become a major
source of reviews about different entities. People presently travel
frequently around the world for different purposes. Seeking good hotels
for accommodation is a prime concern. Customer reviews on hotels help
future customers to take decisions about their accommodation as well as
help hotel owners to rethink about designing customer facilities.
However, many online reviews are biased due to different factors. Many
hotel owners come up with attractions like referral rewards, coupons,
bonus points etc. to the reviewers to motivate them in writing biased
reviews. We have worked on US’s 100 hotel and found 952 incentivized
reviews out of 19175 reviews, which is 4.96% of total reviews. A
categorization on incentivized reviews is performed as well.
Furthermore, hotels are distinguished based on real and incentivized
reviews found on them. Results are verified using machine learning
algorithms. Random Forest, K-Nearest Neighbor and Support Vector Machine
are applied as machine learning algorithms to validate the accuracy of
our model and their prediction results are compared. Random Forest
outperforms with 94.4% prediction accuracy.