Identifying Significant Customer Opinion Information of Each Aspect from Hotel Reviews

Recognizing whether customers like or dislike a product or service from online reviews may not be sufficient for other customers to make decisions or for owners to improve their merchandising. This was taken up as a challenge in this study that focused on finding significant sentiment information from customer reviews on each hotel aspect. The proposed framework first separated customer reviews into sentences, and then assembled all customer review sentences relating to each aspect of customer reviews using the k-means clustering. Later, those customer sentences are classified them into positive and negative sentiment polarity classes. The classifier was developed by Support Vector Machines (SVM). This can help other customers or the owner to understand why customers like or dislike a particular hotel aspect. The experimental results were evaluated using recall, precision, F1 and accuracy. The clustering method returned satisfactory results of 0.81, 0.80, 0.80 and 0.80, respectively. Meanwhile, the classification method also gave satisfactory results at 0.81, 0.79, 0.80 and 0.79, respectively. Compared to the baseline using F1 and accuracy, our proposed method produces very similar experimental results to the baseline method but our proposed method requires less computational time than the baseline.