A Machine Learning Approach to Detect Customer Satisfaction From
  Multiple Tweet Parameters
Md Mahmudul Hasan Dr. Shaikh Anowarul Fattah
Since internet technologies have advanced, one of the primary factors in
company development is customer happiness. Online platforms have become
prominent places for sharing reviews. Twitter is one of these platforms where
customers frequently post their thoughts. Reviews of flights on these platforms
have become a concern for the airline business. A positive review can help the
company grow, while a negative one can quickly ruin its revenue and reputation.
So it's vital for airline businesses to examine the feedback and experiences of
their customers and enhance their services to remain competitive. But studying
thousands of tweets and analyzing them to find the satisfaction of the customer
is quite a difficult task. This tedious process can be made easier by using a
machine learning approach to analyze tweets to determine client satisfaction
levels. Some work has already been done on this strategy to automate the
procedure using machine learning and deep learning techniques. However, they
are all purely concerned with assessing the text's sentiment. In addition to
the text, the tweet also includes the time, location, username, airline name,
and so on. This additional information can be crucial for improving the model's
outcome. To provide a machine learning based solution, this work has broadened
its perspective to include these qualities. And it has come as no surprise that
the additional features beyond text sentiment analysis produce better outcomes
in machine learning based models.