Update README.md

README.md

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 ---
 language:
 - en
+license:
+- cc-by-nc-4.0
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -53,30 +55,25 @@ dataset_info:
   dataset_size: 31840239
 ---
 # Dataset Card for "tripadvisor-hotel-reviews"
-
 ## Dataset Description
-
 - **Homepage:** Kaggle Challenge
-- **Repository:** https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset?select=True.csv
-- **Paper:** N.A.
+- **Repository:** https://www.kaggle.com/datasets/andrewmvd/trip-advisor-hotel-reviews
+- **Paper:** https://zenodo.org/record/1219899
 - **Leaderboard:** N.A.
 - **Point of Contact:** N.A.
-
 ### Dataset Summary
-
-Can you use this data set to make an algorithm able to determine if an article is fake news or not ?
-
+Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
+With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
+Citations on a scale from 1 to 5.
 ### Languages
-
 english 
-
 ### Citation Information
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-Acknowledgements
-
-Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.
-Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138).
-
+If you use this dataset in your research, please credit the authors.
+Citation
+Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.
+DOI
+License
+CC BY NC 4.0
+Splash banner
 ### Contributions
-
-Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.
+Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.