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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (3): 62-71    DOI: 10.11925/infotech.2096-3467.2017.03.08
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The Impacts of Reviews on Hotel Satisfaction: A Sentiment Analysis Method
Wu Weifang, Gao Baojun(), Yang Haixia, Sun Hanlin
Economics and Management School, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper analyzes the online hotel reviews to identify the factors influencing the customer’s satisfaction, and then provides suggestion to the management. [Methods] First, we extracted features and reduced dimensionality of travelers’ comments from Tripadvisor.com with the help of Word2Vec technique. Secondly, we extracted the characteristics of each type of the corresponding emotion based on sentiment analysis technology. Finally, we constructed an econometric model to analyze the correlation between the hotel reviews and users’ satisfaction. [Results] We found that positive reviewers were generally satisfied with the hotel service, however, there was no linear relations between the two factors. The more feature categories mentioned by the user in comments, the more likely he or she was not satisfied. The consumers paid more attention to the staff of the luxury hotels, while cared the cleanliness of the economic ones. Consumers’ attitudes towards luxury hotels were significantly affected by the Internet, which posed less obvious influences to the economic ones. [Limitations] The sample was not comprehensive, and more studies are needed to analyze data from multiple cities. [Conclusions] This study lays theoretical foundation for the online word-of-mouth research from the perspective of user generated contents.

Key wordsComment Text      Hotel Features      Sentiment Analysis      Consumer Satisfaction     
Received: 05 December 2016      Published: 20 April 2017
ZTFLH:  F59 G350  

Cite this article:

Wu Weifang,Gao Baojun,Yang Haixia,Sun Hanlin. The Impacts of Reviews on Hotel Satisfaction: A Sentiment Analysis Method. Data Analysis and Knowledge Discovery, 2017, 1(3): 62-71.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.03.08     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I3/62

reviewid Opinion Unit Word_count Label
155764734 the rooms were a great size and layout 8 Facility
155344163 it is fairly clean 4 Cleanliness
117415795 the food was great 2 Food
117474538 the location at the cosmo is great 7 Location
117490549 food amazing 2 Food
118435844 great value for the money 5 Value
118683963 internet is free and fast 5 Internet
143482015 staff was very friendly and helpful. 6 Staff
特征 Naive Bayes SVM
Precision Recall Precision Recall
Cleanliness 74% 78% 75% 77%
Facility 78% 64% 82% 82%
Food 87% 83% 86% 83%
Internet 73% 88% 74% 86%
Location 65% 84% 69% 85%
Staff 88% 85% 87% 87%
Value 80% 80% 80% 81%
Total Accuracy 79% Accuracy 80%
Statistic N Mean St.Dev. Min Pctl
(25)
Median Pctl
(75)
Max
food_senti 5124 0.21 0.44 -1.64 0 0 0.39 3.05
facilities_senti 5124 0.6 1.05 -4.65 0 0.41 1.12 11.1
value_senti 5124 0.11 0.45 -2.52 0 0 0.22 3.78
staff_senti 5124 0.35 0.73 -4.02 0 0.12 0.71 8.17
cleanliness_senti 5124 0.2 0.56 -2.8 0 0 0.41 4.16
location_senti 5124 0.21 0.38 -1.45 0 0 0.38 2.77
internet_senti 5124 0.05 0.28 -1.74 0 0 0 4.49
location 4310 4.31 0.96 1 3 4 5 5
rooms 4356 3.96 1.17 1 3 4 5 5
value 4862 3.87 1.21 1 3 4 5 5
cleanliness 4842 3.94 1.21 1 3 4 5 5
sleepquality 4074 4.03 1.18 1 3 4 5 5
ave_sentiment 5124 0.24 0.26 -1.53 0.08 0.23 0.39 2.29
AvgRating 5124 3.78 1.22 1 3 4 5 5
Food Facilitity Value Staff Clean Location Internet
Food 1 0.18 0.09 0.13 0.14 0.09 0.07
Facilitity 1 0.06 0.22 0.15 0.18 0.07
Value 1 0.10 0.13 0.04 0.11
Staff 1 0.16 0.12 0.08
Cleanliness 1 0.06 0.11
Location 1 -0.003*
Internet 1
Location Rooms Value Clean SleepQuality
Location 1 0.61*** 0.43** 0.49*** 0.53***
Rooms 1 0.57*** 0.73*** 0.72***
Value 1 0.62*** 0.61***
Cleanliness 1 0.65***
SleepQuality 1
Dependent variable: as.factor(AvgRating)
(1) (2) (3) (4)
y≥2 2.0906*** 1.8908*** 2.3932*** 2.1126***
-0.0571 (0.0747) (0.0925) (0.0600)
y≥3 1.0706*** 0.8228*** 1.3668*** 0.9845***
-0.0438 (0.0589) (0.0686) (0.0457)
y≥4 -0.2140*** -0.7763*** 0.3546*** -0.4444***
-0.0402 (0.0577) (0.0605) (0.0429)
y≥5 -1.6895*** -2.5997*** -0.9835*** -1.9997***
-0.0456 (0.0735) (0.0626) (0.0494)
food_senti 0.3006*** 0.4529*** 0.4552***
-0.0626 (0.0786) (0.1131)
facility_senti 0.6049*** 0.5666*** 0.4389***
-0.0296 (0.0415) (0.0440)
value_senti 0.3540*** 0.5665*** 0.6579***
-0.0599 (0.0724) (0.1231)
staff_senti 0.7608*** 0.8931*** 0.7486***
-0.0424 (0.0592) (0.0651)
cleanliness_senti 0.4665*** 0.6457*** 0.7906***
-0.0499 (0.0604) (0.1069)
location_senti 0.5236*** 0.4926*** 0.3709**
-0.0731 (0.0984) (0.1138)
internet_senti 0.0624 0.3743*** -0.1412
-0.0964 (0.1049) (0.3502)
ave_sentiment 6.7401***
(0.1954)
sentiment^2 -3.7624***
(0.2259)
Num_of_feature -0.0802***
(0.0178)
Observations 5, 124 2, 625 2, 499 5, 124
R2 0.2571 0.3437 0.2087 0.3229
chi2 (df = 7) 1 424.3920*** 1 037.0600*** 538.1301*** 1 863.1670***
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