| Abstract |
Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP),
which utilizes the polarity classification of reviews expressed at the aspect, sentence or document
level. Several businesses and organizations utilize this technique to improve production, as well as
employee and service efficiency. However, the users’ reviews in our study were expressed in an
unstructured data form, which contained spelling errors, leading to complex classifications for both
the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML)
algorithms can be applied to the data extraction, where classification polarity can be categorized into
a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine
the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in
Thai, which is a low-resource language. The dataset was collected manually from two online agencies
(Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing
steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram,
and Word2Vec techniques were applied to convert the text reviews into vectors, processed with
different ML algorithms, to determine sentiment polarity classification and to make accurate
comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold
cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The
experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique
was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the
highest accuracy of 89.96%. The results of this research can be applied as the tool for small and
medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel
domain. |
Publication
Journal Publication
ไทย