# Service-Aware Interactive Presentation of Items for Decision-Making
## Materials and Methods
### Review Analysis
#### 3.2.3 Binding Review Sentences to Evaluation Dimensions  
In order to support the retrieval of reviews that are relevant to a specific evaluation dimension \(d\in\mathcal{D}\), and to summarize consumer satisfaction, the sentences of \(r\) have to be associated to the dimensions they deal with. For this purpose, we analyze each sentence \(s\) of \(r\) and we match its lemmatized noun phrases \(\{lnp_{1},\ldots,lnp_{m}\}\) to the thesauri \(\{\mathcal{T}_{d_{1}},\ldots,\mathcal{T}_{d_{s}}\}\) defined in Section 3.1. A sentence \(s\) matches a thesaurus \(\mathcal{T}\) if it contains at least one noun phrase \(lnp\in\{lnp_{1},\ldots,lnp_{m}\}\) that includes a lemmatized keyword of \(\mathcal{T}\). Having identified the thesauri matched by \(s\), we bind \(s\) and we index it under the corresponding dimensions for quick retrieval at information presentation time. When this step of the pipeline is completed, each sentence \(s\) of \(r\) is associated to a list of lemmatized noun phrases \(\{lnp_{1},\ldots,lnp_{m}\}\) and to a set of referred dimensions \(\{d_{1},\ldots,d_{k}\}\) as shown in Listing 1. ``` reviewid:x; satisfaction:y; sentence1: lemmatized noun phrases: \(lnp_{1},\ldots,lnp_{m}\); referred dimensions: \(d_{1},\ldots,d_{k}\); sat:\(x_{1}\); sentence2: noun phrases:...; referred dimensions:...; sat:...; sentence3:...

Figure 2: Pipeline of activities for the analysis of an individual review. #### 3.2.4 Sentiment Analysis

The sentiment analysis step is aimed at identifying the positive, neutral, or negative polarity emerging from review text. Combined with the identification of the dimensions referenced by the sentences of the review \(r\), this makes it possible to estimate \(r\)'s author evaluation of item \(i\in\mathcal{L}\). We perform this analysis at two granularity levels:

* _Sentiment of the review:_ this is aimed at extracting the reviewer's overall sentiment about \(i\), balancing the possibly different opinions that emerge from the individual sentences included in \(r\). For instance, the reviewer might be happy about certain aspects of item \(i\) and unhappy about other ones, conveying a neutral overall evaluation in \(r\). We compute the sentiment of \(r\) as the polarity of its text by using the TextBlob Python library [89]. This library leverages the Pattern library [90] that takes into account the individual word scores from SentiWordNet[91] and uses heuristics for negation to compute the overall polarity of a text. * _Sentiment of sentences by evaluation dimension:_ this is aimed at extracting the sentiment of the reviewer concerning the considered evaluation dimension. For each sentence \(s\) of \(r\), for each dimension \(d\in\mathcal{D}\) addressed in \(s\), the sentiment of \(s\) for \(d\) is computed as the polarity of \(s\) using the TextBlob library on the text of \(s\). As the polarity is defined in the [\(-\)1, 1] interval we normalize it to a level of satisfaction in [0, 100] in order to be presented as a percentage in the user interface of the information visualization system. Listing 1 shows the output of the review analysis pipeline. Each review \(r\) is represented by means of a data structure reporting its identifier (id) and satisfaction level (satisfaction, denoted as \(satisfaction_{r}\) in the following). As previously described, for each sentence \(s\) of the review, the data structure reports its lemmatized noun phrases, referred dimensions, and the satisfaction level of \(s\) (sat). ### Item Evaluation

The INTEREST model enables the user to select a time frame and a set of evaluation dimensions that determine a set of relevant reviews \(\mathcal{R}=\{r_{1},\dots,r_{w}\}\) to be considered. Given this set, our model aggregates review data to summarize consumer feedback about the item \(i\in\mathcal{L}\).