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Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip

3 This study presents a data mining approach for modeling TripAdvisor score using 504 reviews 4 published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features 5 characterizing the reviews, hotels and the users were prepared and used for feeding a support 6 vector machine for modeling the score. The results achieved reveal the model demonstrated 7 adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for 8 extracting useful knowledge translated into features’ relevance for the score. The findings 9 unveiled user features related to TripAdvisor membership experience play a key role in 10 influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the 11 day of the week were found to influence scores. Such knowledge may be helpful in directing 12 efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews 13 according to the member and review date. 14

published on platforms such as TripAdvisor, being directly influenced by scores granted by other 140 users, even though reputation attacks seem to occur often in the hospitality industry (Filieri et al.,   200 After defining the problem, data collection and preparation is the next key step for compiling a 201 dataset that serves as input for modeling. Such dataset is the building block essential for 202 unveiling knowledge through a data mining modeling technique. Moreover, the dataset needs to 203 be composed of a table where each row represents an instance of the problem being addressed 204 and each column represents a feature that characterizes that instance (Witten & Frank, 2005).

205
Since TripAdvisor owns several domains to cover suffixes from several countries, the data was 206 collected from the TripAdvisor.com website, as the .com is considered the base site where there 207 are reviews belonging to users from every part of the world. Then, it was necessary to filter the 208 information by location, i.e. Las Vegas, Nevada, and more specifically filtering by hotels in the 209 Strip avenue. Las Vegas, the so called city of sin, born eighty years ago over a desert where 210 hotels started to be built and forming one of the most entertaining cities in the world, is driven by of the major hotel's positioning, whereas the city's success as a gaming destination due to the 216 government and private institutions was proposed and analyzed by Lee (2015). Given the interest 217 triggered by Las Vegas hospitality, a large number of reviews are available, which is a 218 requirement for the proposed data-driven study. The present research started by collecting all the 219 features available on TripAdvisor's webpages from several online reviews published during 220 2015 and targeting hotels located in the Strip avenue.

221
As a result, a list of 21 different hotels was displayed, allowing to choose a hotel at a time in 222 order to extract the data from each one of them. When opening one of the chosen hotels' pages, 223 access is gained to various information regarding the hotel, such as its address, general quality 224 rating, individual reviews, photos and videos from both the hotel and the previous customers and 225 also the hotel's features. Once the hotel was selected, the procedure undertaken consisted in 226 collecting the data by extracting two reviews per month from the year of 2015, repeating this 227 process for all the 21 hotels. The uniform distribution of the reviews spanned through the different months provided data for building a model that also considered the seasonality effect as it is shown in Figure 1 where each square represents a fragment of data collected. The textual 235 review was also collected, in case it would be needed in future research. The numbers identify 236 the feature extracted enumerated under parenthesis in the column "origin" of Table 1.  To obtain the date the user has registered in TripAdvisor, it was enough to pass with the cursor 240 over the username to get such additional information, displayed in Figure 2. 241 Finally, the webpage with the information supplied by TripAdvisor for each of the 21 hotels was 242 accessed to gather relevant features from each hotel (e.g., the link for the Bellagio is:      types of values that can be assumed by each feature, with the categorical type corresponding to a 266 fixed number of enumerated values (e.g., the "gym" feature can assume "yes" or "no") and the 267 numerical type corresponding to an ordinal numbered feature. Dates are a particular type of 268 numerical features due to its format restrictions, while "text" type corresponds to unstructured 269 data (here reserved for the "review text"). After the data collection process, the dataset contained 504 records and 21 extracted features (as 273 of "origin=extracted", from Table 1)  reviews from native English countries. The username was also excluded, as most of the reviews 289 were from different users (only six of the reviews were made by users from which a previous 290 review was also selected for the dataset). Finally, the textual content of the reviews was not 291 considered for modeling, since it is unstructured and additional techniques would need to be 292 employed, such as text mining. Furthermore, the focus of this research is on knowledge 293 extraction from quantitative features to overcome the limitations of textual reviews mentioned in 294 Section 2, such as the ambiguity of human language.

295
Another procedure that usually takes place in data mining is feature engineering, which is 296 considered a key step by Domingos (2012). Therefore, a few of the features were transformed 297 (Table 1, "status=transformed") into new ones, which were computed (Table 1, 298 "origin=computed"). For example, the year when the user registered as a TripAdvisor member is 299 just an occurrence in time, whereas the number of years of membership represents how long the 300 user is active in TripAdvisor. Thus, the "member registered year" was transformed in "member 301 years". The same happened for "review date", from where "review month" and "review 302 weekday" were computed. Also, the country from where the reviewer is native was used to 303 obtain the corresponding continent, although in this case the "country" feature was kept, since it 304 may conceal meaningful value through user country's characterization of the review score.

305
The result of these data collection and preparation procedures is a dataset with a total of 19 input 306 features plus the outcome to predict, the score given by users (Table 1 features with   307 status="included").   Nevertheless, such issue does not apply to the present study, since the outcome varies from 1 to 370 5.

375
With the dataset ready for modeling, a procedure took place to assess the robustness of the model 376 built on the data. Figure 5 shows

412
As described in Section 3 and illustrated in Figure 5, modeling performance was first assessed 413 using an evaluation scheme including a realistic 10-fold cross-validation procedure to test the 414 model with unforeseen data, which was ran twenty times. Table 2 shows the predictions for three 415 randomly selected reviews with the data used as an input to the model (data is displayed  understanding what drives users to publish a given score can ultimately leverage managerial 440 decision support in hospitality. Therefore, the understanding of the factors that influence why a 441 given hotel is being rated with a certain score can be valuable for managers to act on parameters 442 they control (e.g., hotel related features) and to preventively manage their units according to the 443 expected tourists' demands (e.g., by knowing the more demanding tourists).    TripAdvisor's review features, with experience counting as the top influencing factor, while 503 hotel brand having a significant lower relevance.

504
After analyzing the relevance of features on TripAdvisor score, it is interesting to dive deeper 505 into each of the most relevant ones (with relevance above 3.5%, as identified in Figure 8) in an 506 attempt to understand how these features affect the score. Both the most relevant ("nr. Hotel 507 reviews") and the fourth most relevant ("nr. Reviews") features overlap in the sense that the 508 latter includes the former, plus the reviews the user has made on attraction units and restaurants.

509
Therefore, these two features are analyzed together. Figure 9 shows how each influence the 510 score. As expected (Magnini et al., 2003), the experience momentum after the initial first reviews 511 tend to turn the customer more demanding when publishing online score. Nevertheless, such   Up to four years of membership, the conclusions are similar to the number of reviews made; 521 however, users registered five years ago or more tend to be more positive by granting better 522 review scores. While for the number of reviews, it can also be observed on Figure 9 a slight 523 increase on the score after a certain threshold (this is particularly visible on the "nr. Reviews" 524 feature), the results for "member years" clearly amplify such tendency, with older members 525 giving scores above new members. Some hypotheses can be raised based on this result. One of 526 the most plausible is that tourists with more experience have better knowledge on the destination 527 and units available, thus they will choose the hotels that please them the most, resulting in higher 528 scores. Also, experienced TripAdvisor members are probably keener to read other members' 529 reviews and so be better informed to make judged decisions on their own stays .

530
Nevertheless, more data would be needed to confirm or reject such hypotheses.

533
The third most relevant feature for modeling score was the period of stay, in quarter fractions of 534 a year. Figure 11 shows the seasonality effect on TripAdvisor score. Several previous studies are   Other features contributing with a relevance below 5% including "helpful votes", "traveler type", 578 "hotel name" and "user country" are not scrutinized in this paper. Nevertheless, each of them suitable user profiles to direct efforts in answering those, aligned with the hotel strategy.

631
Moreover, hotel managers can optimize answering reviews by framing template responses 632 according to users' profiles, leading to an efficiency improvement by directing efforts of team 633 members. In alignment with the same recommendation, efforts in answering online reviews may 634 be redirected to answering the more negative reviews during the middle of the week, considering 635 the observed influence of such feature. However, additional studies would need to be conducted 636 in order to adjust such proposed reviews' answering strategies.

637
It should be noted that, by being a location-based study, users' awareness of Las Vegas brand 638 itself must be an accountable factor on influencing score. Furthermore, such renowned brand is 639 able to generate controversial feelings capable of affecting tourists' perception. This fact may 640 also play a role on the lower ranked hotel features in terms of relevance when compared to user 641 characteristics. As Magnini et al. (2003) discussed, customer satisfaction may bias a data mining approach in tourism due to the relative importance each user attributes to certain characteristics.

643
The present study sheds additional light by concluding that experience as a TripAdvisor member 644 does affect the score rank given by users. However, the present study is focused solely on 645 reviews for hotels in Las Vegas Strip, thus its conclusions have to remain location-based.