E-commerce websites allow users to share opinions about a product/service by providing textual reviews along with numerical ratings.
Users also behaved more active in opinion bar chart by manipulating the extracted features while less frequently viewing the raw textual reviews.
The performance of the proposed approach returned high accuracy and the results revealed that when large datasets are concerned, only a fraction of the data is needed for creating a system to predict the review ratings of textual reviews.
The system uses both textual reviews and ratings from TripAdvisor.
The platform allows choosing flights or hotels, reading personal reviews of hotels, classifying hotels using textual reviews and ratings as well as planning new travels.
Tourism crowdsourcing platforms, e.g. Expedia and TripAdvisor, collect large volumes of feedback data regarding tourism resources, including multi-criteria ratings, textual reviews and photographs.
The article is structured as follows: in "Related work" section we overview some works related to the use of users' textual reviews in the recommendation process; in "The proposed system" section we describe the proposed system.
Individuals create a digital footprint while using web services to organise trips, i.e. to search, book and share their opinions in the form of ratings, textual reviews, photographs, etc.
As future work, we intend to: (1) explore multi-criteria recommendation using both textual reviews and multi-criteria ratings and (2) explore the users' reputation factor to model incoming ratings.
The textual narrative review also described gaps in the literature, both by showing where evidence was absent and by making an evaluation of the strength of evidence in different areas.
