# Service-Aware Interactive Presentation of Items for Decision-Making
## Materials and Methods
### Review Analysis
#### 3.2.3 Binding Review Sentences to Evaluation Dimensions
## Validation Methodology

We validate our model on the home booking domain with particular reference to the information available in Airbnb [26], which is increasingly used by people to find homes for their holidays, as well as for working purposes, and thus provides consumer data referring to a rich spectrum of evaluation contexts. Similar to other services such as Booking [92], and as a measure to strengthen the robustness of the underlying Reputation Management System [93], Airbnb enables each consumer to upload at most one review for each renting contract after having left the apartment. This guarantees that online feedback is based on truly experienced services. ### Dataset

The experiments are based on a portion of a large, public dataset of reviews collected in the city of Florence (IT) by the Airbnb home booking service (the dataset is periodically updated and can be downloaded from [[http://insideairbnb.com/get-the-data.html](http://insideairbnb.com/get-the-data.html)]([http://insideairbnb.com/get-the-data.html](http://insideairbnb.com/get-the-data.html))) [26]. The dataset stores information about hosts, homes (denoted as \"listings\"), and their neighborhood; moreover, it stores the textual feedback uploaded by visitors. The following information about reviews is provided. * listing_id: numerical identifier of the home evaluated in the review. * id: numerical identifier of the review. * date: timestamp of the review. * reviewer_id: numerical identifier of the author of the review. * reviewer_name: name of the author of the review. * comments: review text in Natural Language. Even though the Airbnb website makes it possible to rate the homes in a 5-point Likert scale, the dataset does not report such ratings. We omit the descriptive statistics of the whole dataset because for our study we focused on a small number of apartments, corresponding to a result list proposed to the user. Within this list, we asked participants to evaluate four specific homes, the same for all people, with the purpose of exposing users to the same amount of data to be analyzed, see Section 4.3. The selected homes have similar size and facilities to be comparable from the viewpoint of basic requirements; e.g., number of rooms. Moreover, each apartment has approximately 15 reviews and the overall amount of text from reviews presented during the evaluation of a home is approximately 50 lines. ### Evaluation Dimensions for the Home Booking Domain

In order to identify the evaluation dimensions, we first designed a Service Journey Map (SJM) describing the home booking experience from the viewpoint of the guest. As in this work we focus on home renter experience, we only model the customer and apartment owner roles. Specifically, we ignore the actors involved in other service stages, such as the visit to the website. With the first SJM we identified four service stages: Visit website, Check-in, Stay in apartment, and Check-out. We associated a separate evaluation dimension to each stage and we built the corresponding thesaurus by using previous studies that describe the topics addressed in the reviews collected by home booking services [27] or by similar ones, such as hotel booking ones [28; 29]. However, after a first analysis we found out the following. * Check-in and check-out are usually related in reviews and they are associated to the same keywords, which appear in both thesauri. * Stay in apartment has a rather large number of keywords. Moreover, in their comments, reviewers frequently separate the aspects related to the apartment interiors (furniture, comfort, services) from those concerning its surroundings, e.g., geographic position, available public transportation, shops, and presence of noise. * The interaction with the host and her/his properties represent a relevant evaluation dimension crossing all the service stages.