Center for Machine Learning and Intelligent Systems 		About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

Travel Reviews Data Set
Download: Data Folder, Data Set Description

Abstract: Reviews on destinations in 10 categories mentioned across East Asia. Each traveler rating is mapped as Excellent(4), Very Good(3), Average(2), Poor(1), and Terrible(0) and average rating is used.

Data Set Characteristics:  

Multivariate, Text

Number of Instances:

980

Area:

N/A

Attribute Characteristics:

Real

Number of Attributes:

11

Date Donated

2018-12-19

Associated Tasks:

Classification, Clustering

Missing Values?

N/A

Number of Web Hits:

6159

Source:

Shini Renjith, shinirenjith '@' gmail.com


Data Set Information:

This data set is populated by crawling TripAdvisor.com. Reviews on destinations in 10 categories mentioned across East Asia are considered. Each traveler rating is mapped as Excellent (4), Very Good (3), Average (2), Poor (1), and Terrible (0) and average rating is used against each category per user.


Attribute Information:

Attribute 1 : Unique user id
Attribute 2 : Average user feedback on art galleries
Attribute 3 : Average user feedback on dance clubs
Attribute 4 : Average user feedback on juice bars
Attribute 5 : Average user feedback on restaurants
Attribute 6 : Average user feedback on museums
Attribute 7 : Average user feedback on resorts
Attribute 8 : Average user feedback on parks/picnic spots
Attribute 9 : Average user feedback on beaches
Attribute 10 : Average user feedback on theaters
Attribute 11 : Average user feedback on religious institutions


Relevant Papers:

Renjith, Shini, A. Sreekumar, and M. Jathavedan. 2018. “Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain”. In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 127–31. IEEE.



Citation Request:

Renjith, Shini, A. Sreekumar, and M. Jathavedan. 2018. “Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain”. In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 127–31. IEEE.

Supported By:

 In Collaboration With:
About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML