Automatic analysis of textual hotel reviews

Abstract

Social Media and consumer-generated content continue to grow and impact the hospitality domain. Consumers write online reviews to indicate their level of satisfaction with a hotel and inform other consumers on the Internet of their hotel stay experience. A number of websites specialized in tourism and hospitality have flourished on the Web (e.g. Tripadvisor). The tremendous growth of these data-generating sources demands new tools to deal with them. To cope with big amounts of customer-generated reviews and comments, Natural Language Processing (NLP) tools have become necessary to automatically process and manage textual customer reviews (e.g. to perform Sentiment Analysis). This work describes OpeNER, a NLP platform applied to the hospitality domain to automatically process customer-generated textual content and obtain valuable information from it. The presented platform consists of a set of Open Source and free NLP tools to analyse text based on a modular architecture to ease its modification and extension. The training and evaluation has been performed using a set of manually annotated hotel reviews gathered from websites like Zoover and HolidayCheck.