Semantic model to extract tips from hotel reviews
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Semantic model to extract tips from hotel reviews Shivendra Kumar1 · C. Ravindranath Chowdary1 Accepted: 26 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Information extraction · Review mining · Tip extraction · Sentiment analysis
* C. Ravindranath Chowdary [email protected] Shivendra Kumar [email protected] 1
Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
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S. Kumar, C. R. Chowdary
1 Introduction With the growth of e-commerce, the online reviews posted by customers have increased significantly. The customer’s decision is often influenced by these reviews while booking the hotel [27]. Reviews play a pivotal role in helping a customer decide whether to book a hotel or not. Consumers read an average of ten online reviews before trusting a local business [23]. It shows that users go through very few reviews to make a decision. The importance of reviews can easily be seen with the help of the pie chart presented in Fig. 1. This pie chart reflects the number of people who read online reviews for businesses [23]. A tip is defined as “short, concrete and self-contained bits of non-obvious advice” by [13]. Many websites, like Yelp, TripAdvisor, offer a small set of tips for each hotel. But they do not portray the same idea as reflected by al
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