| dc.description.abstract | Sentiment analysis or opinion mining
refers to the process of identifying people’s
sentiments, opinions, attitudes and emotions
behind a written text. In recent years, sentiment
analysis studies have become an active research
area under natural language processing.
Understanding the opinion behind the usergenerated
text can be applied to various
applications. When it comes to the hotel sector
and travel planning, user reviews and comments
are quite useful. Therefore, guest reviews are
becoming a prominent factor, which influence
people’s booking decisions. In addition, knowing
about these comments is important for quality
control of the hotel management too, because it
may be worth checking out some stats over time.
The fundamental objective of this research is to
compare several machine learning classifiers
and find out the best classifiers to develop a
sentiment analysis model for the hotel reviews,
to tackle customers’ sentiment. Under this
research, a comparative analysis was established
among Multinomial Naïve Bayes (MNB),
Bernoulli Naïve Bayes (BNB), Logistic
Regression (LR), Stochastic Gradient Descent
Classifier (SGD), Linear Support Vector Classifier
(SVC), Random Forest Classifier and Multi-layer
Perceptron Classifier (MLP) classifiers.
Moreover, two feature extraction techniques
called Count Vectorizer and Term Frequency
Inverse Document (TF-IDF)) are also compared
to find out the best approach to perform the
feature extraction. The result from this research
shows that the highest results were obtained in
Logistic Regression with TF-IDF method
(Accuracy 87.39%) and SGD algorithms with TFIDF
(Accuracy 87.71%), while the lowest
accuracy was obtained for Bernoulli NB classifier
with Count Vectorizer (Accuracy 64.67%). Every
time when using Count Vectorizer as the feature
extraction method, the accuracies decreased,
than when the TF-IDF method was used. | en_US |
