# Service-Aware Interactive Presentation of Items for Decision-Making
## Materials and Methods
### Review Analysis
#### 3.2.3 Binding Review Sentences to Evaluation Dimensions
Specifically, the level of satisfaction with \(i\) for each evaluation dimension \(d\in\mathcal{D}\) (denoted as \(sat_{il}\)) is computed as the mean satisfaction level of the sentences \(\mathcal{S}_{di}\) that are included in the reviews of \(\mathcal{R}\) and are indexed under \(d\):

\[sat_{di}=\frac{1}{|\mathcal{S}_{di}|}\sum_{s\in\mathcal{S}_{di}}sat_{s} \tag{1}\]

Our model also supports the generation of an _overall satisfaction_ level about item \(i\) by computing the mean satisfaction of the reviews belonging to \(\mathcal{R}\):

\[overallSatisfaction_{i}=\frac{1}{|\mathcal{R}|}\sum_{r\in\mathcal{R}}satisfaction _{r} \tag{2}\]

All satisfaction levels take values in [0, 100]. ### Data Visualization

Our model can present consumer feedback at different granularity levels to flexibly support the user in the assessment of item suitability. Specifically, given \(i\in\mathcal{L}\):

* The structured review representation generated by the review analysis pipeline makes it possible to generate dynamic charts that show the overall satisfaction level about \(i\), as well as the satisfaction about specific evaluation dimensions in \(\mathcal{D}\). * The indexing of review sentences under specific dimensions of \(\mathcal{D}\) supports a direct and efficient access to the reviews that address the evaluation criteria selected by the user. * The computation of the satisfaction level of individual reviews makes it possible to visually annotate them for fast interpretation. * By exploiting the thesauri, the words of the reviews that make reference to the various evaluation dimensions can be identified and highlighted. Figure 3 shows a portion of the user interface of the Apartment Monitoring application, which we use to simplify the presentation of the visual model we propose. * The left panel is organized as follows. * At the top, there is the menu for selecting the item to be evaluated out of the list \(\mathcal{L}\) proposed to the user, and the link to view the home on the Airbnb web site. * At the bottom, a graphical widget supports the selection of the time frame of analysis. * In the middle, a component includes a checkbox for each evaluation dimension that the user can choose to explore the item. Each dimension is associated with the mean level of

Figure 3: Apartment Monitoring: visualization of consumer satisfaction about a home regarding the host appreciation, in-apartment experience, and surroundings, during the last year. satisfaction derived from the whole set of reviews that belong to the selected time frame. For example, the visualized item has 71% level of satisfaction regarding the host appreciation. * The right panel of the user interface shows the detailed information about the item:
* A histogram visually represents evaluation dimensions by breaking the time frame selected by the user into sub-intervals to overview the temporal distribution of consumer satisfaction. Each bar of the histogram shows the level of satisfaction concerning the associated dimension within its own time interval. The exact level can be visualized by placing the mouse over the bar. * Below the chart there is the list of reviews used for the analysis. These reviews depend on the chosen time frame and on the dimensions selected using the checkboxes. The reviews posted in the same time interval which do not address those dimensions are not shown. In each review, a scale of smilies displays its satisfaction level; moreover, the words that correspond to the selected dimensions are highlighted using color coding. The bars of the histogram are interactive: if the user clicks on any of them, the visualized set of reviews is refreshed to only include the relevant ones and the highlighted words are updated to maintain the consistency with her/his selections. Thus, the user can dynamically filter reviews, e.g., (s)he might investigate why the in-apartment experience received a low evaluation in May and in September 2019.