Semantic model to extract tips from hotel reviews

E-commerce is growing at a swift pace, and the related content on the web is exploding. This is due to the shift of a massive amount of sales and bookings to the online platform. A large number of customers now prefer e-commerce to buy products or online booking for stays. After their transactions, customers post their experiences in the form of text reviews. Further, a new customer usually goes through these reviews before making an online transaction. However, many of such reviews include less important and often redundant information. This work aims to generate short pieces of useful text (‘tip’) from the large number of reviews which portray not only the relevant and unique information but also the sentiment captured from the reviews. The main motivation to generate a set of tips is to enable new customers to differentiate between competing for similar businesses. Our Tip Extraction Algorithm builds upon the existing methods by including the sentiments captured from the reviews. The proposed algorithms also emphasize the number of reviews for similarity comparison, so that proper weight could be given to amenities or other reviews‘ content. Recommender systems do not consider most of the recent businesses due to the vastness of the number of reviews of well established businesses. We compare our proposed method with the state-of-the-art TipSelector Algorithm for hotel tip extraction, on hotel reviews obtained from the TripAdvisor website. The proposed method works well, even when the number of available reviews is very less. Experimental results show significant improvements over the current state-of-the-art.