Sentiment Analysis on Students’ Satisfaction Towards University

The arrival of social media platforms has changed the way people express themselves.
They share their opinions or perceptions towards products and services online and in
real-time. With the abundance of publicly available data, it could be used to perform
sentiment analysis that has the capability to classify each data into positive, negative
and neutral sentiment. Developing a sentiment analysis model to analyse how people
talk about UTP brands could enhance their marketing strategy. Issues related to
facilities and services such as poor facilities, expensive annual fees, expensive
cafeteria food would be a key highlight to this project. Several scopes has been
identified for this project development. Besides, this paper reports on methodology
that would be implemented for developing sentiment analysis model and it would
adopt waterfall model. Sentiment analysis requires dataset to undergo data pre�processing and data classification. Due to its capability to outperform other classifier,
Naïve Bayes classifier has been chosen to perform the classification process. In the
end, data would be presented in a form of smart chart.