heisguyy / 16-hotel-reviews

This is an open source project for the stage E of the Hamoye Data Science Internship program, cohort 2020, with real life applications in the health, engineering, demography, education and technology.

515K Hotel Reviews Data in Europe

Can you make your own trip more cozy by using data science?

Content

The csv file contains 17 fields. The description of each field is as below:

1.Hotel_Address: Address of hotel.

2. Review_Date: Date when reviewer posted the corresponding review.

3. Average_Score: Average Score of the hotel, calculated based on the latest comment in the last year.

4. Hotel_Name: Name of Hotel

5. Reviewer_Nationality: Nationality of Reviewer

6. Negative_Review: Negative Review the reviewer gave to the hotel. If the reviewer does not give the negative review, then it should be: 'No Negative'

7. ReviewTotalNegativeWordCounts: Total number of words in the negative review.

8. Positive_Review: Positive Review the reviewer gave to the hotel. If the reviewer does not give the negative review, then it should be: 'No Positive'

9. ReviewTotalPositiveWordCounts: Total number of words in the positive review.

10. Reviewer_Score: Score the reviewer has given to the hotel, based on his/her experience

11. TotalNumberofReviewsReviewerHasGiven: Number of Reviews the reviewers has given in the past.

12. TotalNumberof_Reviews: Total number of valid reviews the hotel has.

13. Tags: Tags reviewer gave the hotel.

14. dayssincereview: Duration between the review date and scrape date.

15. AdditionalNumberof_Scoring: There are also some guests who just made a scoring on the service rather than a review. This number indicates how many valid scores without review in there.

16. lat: Latitude of the hotel

17. lng: longtitude of the hotel

Here is a link to the dataset