A System for Aspect-based Opinion Mining of Hotel Reviews
Isidoros Perikos, Konstantinos Kovas, Foteini Grivokostopoulou, Ioannis Hatzilygeroudis
2017
Abstract
Online travel portals are becoming important parts for sharing travel information. User generated content and information in user reviews is valuable to both travel companies and to other people and can have a substantial impact on their decision making process. The automatic analysis of used generated reviews can provide a deeper understanding of users attitudes and opinions. In this paper, we present a work on the automatic analysis of user reviews on the booking.com portal and the automatic extraction and visualization of information. An aspect based approach is followed where latent dirichlet allocation is utilized in order to model topic opinion and natural language processing techniques are used to specify the dependencies on a sentence level and determine interactions between words and aspects. Then Naïve Bayes machine learning method is used to recognize the polarity of the user’s opinion utilizing the sentence’s dependency triples. To evaluate the performance of our method, we collected a wide set of reviews for a series of hotels from booking.com. The results from the evaluation study are very encouraging and indicate that the system is fast, scalable and most of all accurate in analyzing user reviews and in specifying users’ opinions and stance towards the characteristics of the hotels and can provide comprehensive hotel information.
References
- Ady, M., & Quadri-Felitti, D. (2015). Consumer research identifies how to present travel review content for more bookings.
- Au, N., Buhalis, D., & Law, R. (2009). Complaints on the online environment-the case of Hong Kong hotels. Information and communication technologies in tourism 2009, 73-85.
- Baka, V. (2016). The becoming of user-generated reviews: Looking at the past to understand the future of managing reputation in the travel sector. Tourism Management, 53, 148-162.
- Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24.
- Bjørkelund, E., Burnett, T. H., & Nørvåg, K. (2012, December). A study of opinion mining and visualization of hotel reviews. In Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services (pp. 229-238). ACM.
- Blei, D. M., & Lafferty, J. D. (2009). Topic models. Text mining: classification, clustering, and applications, ed. Srivastava, A.N., & Sahami M. CRC Press, Boca Raton, FL, 10(71), 34.
- Cercel, D. C., & Trausan-Matu, S. (2014). Opinion Propagation in Online Social Networks: A Survey. In Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) (p. 11). ACM.
- De Marneffe, M. C., MacCartney, B., & Manning, C. D. (2006, May). Generating typed dependency parses from phrase structure parses. In Proceedings of LREC (Vol. 6, No. 2006, pp. 449-454).
- Freitas, L. A., & Vieira, R. (2013, May). Ontology based feature level opinion mining for portuguese reviews. In Proceedings of the 22nd International Conference on World Wide Web (pp. 367-370). ACM.
- Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.
- Han, H. J., Mankad, S., Gavirneni, N., & Verma, R. (2016). What guests really think of your hotel: Text analytics of online customer reviews. Cornell Hospitality Report, 16 (2), 3-17.
- Hu, Y. H., Chen, Y. L., & Chou, H. L. (2017). Opinion mining from online hotel reviews-A text summarization approach. Information Processing & Management, 53(2), 436-449.
- Liu, B. (2010). Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition (pp. 627-666). Chapman and Hall/CRC.
- Liu, B., 2015. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press
- Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining text data (pp. 415- 463). Springer US.
- Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
- Mellinas, J. P., María-Dolores, S. M. M., & García, J. J. B. (2016). Effects of the Booking. com scoring system. Tourism Management, 57, 80-83.
- Mellinas, J. P., María-Dolores, S. M. M., & García, J. J. B. (2016). The use of social media in hotels as indicator of efficient management. Tourism & Management Studies, 12(2), 78-83.
- Moghaddam, S., & Ester, M. (2011, July). ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 665-674). ACM.
- Moghaddam, S., & Ester, M. (2012, October). On the design of LDA models for aspect-based opinion mining. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 803-812). ACM.
- Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems, 89, 14-46.
- Shanahan, J. G., Qu, Y., & Wiebe, J., 2006. Computing attitude and affect in text: theory and applications, vol. 20, Springer.
- Tian, X., He, W., Tao, R., & Akula, V. (2016). Mining Online Hotel Reviews: A Case Study from Hotels in China.
- Vinodhini, G., & Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6), 282-292.
- Wang, X., & Fu, G. H. (2010, July). Chinese subjectivity detection using a sentiment density-based naive Bayesian classifier. In Machine Learning and Cybernetics (ICMLC), 2010 International Conference on (Vol. 6, pp. 3299-3304). IEEE.
- Wu, Y., Wei, F., Liu, S., Au, N., Cui, W., Zhou, H., & Qu, H. (2010). OpinionSeer: interactive visualization of hotel customer feedback. IEEE transactions on visualization and computer graphics, 16(6), 1109- 1118.
- Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3), 6527-6535.
Paper Citation
in Harvard Style
Perikos I., Kovas K., Grivokostopoulou F. and Hatzilygeroudis I. (2017). A System for Aspect-based Opinion Mining of Hotel Reviews . In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, pages 388-394. DOI: 10.5220/0006377103880394
in Bibtex Style
@conference{webist17,
author={Isidoros Perikos and Konstantinos Kovas and Foteini Grivokostopoulou and Ioannis Hatzilygeroudis},
title={A System for Aspect-based Opinion Mining of Hotel Reviews},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={388-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006377103880394},
isbn={978-989-758-246-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - A System for Aspect-based Opinion Mining of Hotel Reviews
SN - 978-989-758-246-2
AU - Perikos I.
AU - Kovas K.
AU - Grivokostopoulou F.
AU - Hatzilygeroudis I.
PY - 2017
SP - 388
EP - 394
DO - 10.5220/0006377103880394