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A wealth of tourism-related data is available on the Internet, particularly on social networking sites (SNSs) like Facebook and Instagram. Big data analytics (BDA) allows this large quantity of data to be processed, supported by machine learning and artificial intelligence, and gain an in-depth understanding of traveller preferences and behaviours. With regard to hotels, the analysis of data from SNSs provides countless actionable insights into customers'socio-demographic features, habits, daily trends and brand attitudes. This enables communication to be perfectly targeted, besides supplying valuable information to improve customer satisfaction. Nevertheless, the study of the implications of the automatic processing of data from SNSs in the hotel industry is still in its embryonic state. In order to demonstrate the utility of BDA to under- stand how hotels leverage SNSs, we conducted an exploratory study on the Instagram accounts - the photo-sharing SNS known worldwide - of eleven Italian hotels. To this end, the average sentiment score, the average length, lexical diversity and word clouds were calculated on textual data, collected with the instagrapi python package and pre-processed leveraging a standard NLP pipeline. These evidenced different stages of implementation of digital communication on SNSs, shorter text-based messages written on Instagram compared to other SNSs, and specific patterns of user engagement in hotel accounts. BDA also provides information about the online self-promotion process: hotel digital communication is clearly connected to destination, and hashtags are chosen to reach the desired community of travellers. |
