## 4 Dataset
### Keyword Extraction  
User reviews constitute one of the most straightforward ways of studying STR prevalence. Even though taste and preference have an indisputably personal character when evaluating a lodging, they may also convey more general information, that falls well beyond the scope of a specific stay. This is vividly pictured in Figure 2, where an actual review from the dataset is displayed. A detailed analysis of the content of this specific review leads to some interesting observations; for the most part, the user in question is evaluating the property (yellow marking) and the interaction with the host (blue marking). Nevertheless, certain remarks about the neighborhood are also present; the availability of shops, restaurants (orange marking) and public transportation (green marking) nearby, as well as a landmark, the President Hotel (purple marking). In general, review content may be grouped in any of the five categories appearing in the legend of Figure 2; that is;  
1. Landmarks or Attractions
2. Transportation
3. Shops
4. Accommodation
5. Host  
Even though the last two categories naturally vary between accommodations and hosts, the first three characterize the neighborhoods and constitute an indication of why certain areas are more popular than others. Therefore, the application of a keyword extraction methodology on review data is going to quantify user preference with respect to the aforementioned categories of interest. Keyword extraction has been discussed in Section 2, where it has been reasoned that the keyword extraction methodology of choice in this work is RAKE (2019), a domain independent algorithm, which counts term appearance and co-occurrence frequencies excluding special words, such as conjunctions and prepositions. Initially, all reviews pertaining to properties within a specific Athens' neighborhood are concatenated into a single document. Then the document is split into a list of words and the  
Figure 2: Example review from the dataset (color online). stopwords (most common words & prepositions like \"and\", \"the\" etc) are removed, getting a list known as content words. In the following step, a square symmetric matrix of content word co-occurrences \(W\) is created; whose \(w_{ij}\) element designates the number of times word \(i\) co-occurs with word \(j\) in a phrase, with the maximum considered phrase length \(l_{p}\) being a hyper-parameter of the approach. Once \(W\) has been computed, then the score \(s_{i}\) of content word \(i\) equals the ratio of its degree \(d_{i}\), i.e., the sum of the number of co-occurrences \(i\) has with any other content word in the text (Equation (1)), over its frequency \(f_{i}\) in text (Equation (2))  
\[d_{i} = \sum_{j}w_{ij} \tag{1}\] \[s_{i} = \frac{d_{i}}{f_{i}} \tag{2}\]  
Content words may be viewed as phrases of length 1. The score of longer phrases (up to length \(l_{p}\)) spotted in the the list of content words ensues from the summation of the scores of the individual words they are comprised of. Finally, in order to filter out rare words and phrases, a minimum frequency \(f_{min}\) is defined for any non-stopword to be included in the list of content words (and of any phrase of length less than \(l_{p}\) to be considered), which constitutes the second hyper-parameter of the approach. After a thorough experimentation procedure, the optimal values of the hyper-parameters have been determined to be \(l_{p}=3\) and \(f_{min}=10\). Table 4 below summarizes the 20 most frequently extracted keywords for the neighborhood of Emporiko Trigono-Plaka, which is the most popular among the City of Athens neighborhoods, as it concentrates the biggest number of listings and the most user reviews.