Abstract
The world is in the midst of a digital revolution, and thus, it is natural that businesses are leveraging technology to position themselves well in the digital market. Travel planning and hotel bookings have become significant commercial applications. In recent years, there has been a rapid growth in online review sites and discussion forums where the critical characteristics of a customer review are drawn from their overall opinion/sentiments. Customer reviews play a significant role in a hotel’s persona which directly affects its valuation. This research work is intended to address the problem of analyzing the inundation of opinions and reviews of hotel services publicly available over the Web. Availability of large datasets containing such texts has allowed us to automate the task of sentiment profiling and opinion mining. In this study, over 800 hotel reviews are collected from travel information and review aggregator site like Trip Advisor, and after pre-processing of collected raw text reviews, various features are extracted using unigram, bigram, and trigram methods. The labeled feature vectors are used to train binary classifiers. The results are compared and contrasted among ensemble classifiers, support vector machines, and linear models using performance measures such as accuracy, F-measure, precision, and recall.
Published Version
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