References
- Akhtar, N., Zubair, N., Kumar, A., & Ahmad, T. (2017, August 22–24). Aspect based sentiment oriented summarization of hotel reviews. Proceedings of the 7th International Conference on Advances in Computing & Communications (pp. 563–571). Cochin, India. https://doi.org/https://doi.org/10.1016/j.procs.2017.09.115
- AL-Smadi, M., Qwasmeh, O., Talafha, B., Al-Ayyoub, M., Jararweh, Y., & Benkhelifa, E. (2016, December 5–7). An enhanced framework for aspect-based sentiment analysis of hotels’ reviews: Arabic reviews case study. Proceedings of the 11th International Conference for Internet Technology and Secured Transactions (pp. 98–103). Barcelona, Spain. https://doi.org/https://doi.org/10.1109/ICITST.2016.7856675.
- Al-Smadi, M., Talafha, B., Al-Ayyoub, M., & Jararweh, Y. (2019). Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. International Journal of Machine Learning and Cybernetics, 10(2019), 2163–2175. https://doi.org/https://doi.org/10.1007/s13042-018-0799-4
- Arnaud, A. (2016). mtranslate (version 1.6) [Computer software]. https://pypi.org/project/mtranslate/
- Chen, F. W., Plaza, A. G., & Urbistondo, P. A. (2017). Automatically extracting tourism-related opinion from Chinese social media. Current Issues in Tourism, 20(10), 1070–1087. https://doi.org/https://doi.org/10.1080/13683500.2015.1132196
- Chiu, C., Chiu, N. H., Sung, R. J., & Hsieh, P. Y. (2015). Opinion mining of hotel customer-generated contents in Chinese weblogs. Current Issues in Tourism, 18(5), 1–19. https://doi.org/https://doi.org/10.1080/13683500.2013.841656
- Geetha, M., Pratap, S., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels – an empirical analysis. Tourism Management, 61(2017), 43–54. https://doi.org/https://doi.org/10.1016/j.tourman.2016.12.022
- Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/https://doi.org/10.1109/MCSE.2007.55
- Kasper, W., & Vela, M. (2011, October 17–19). Sentiment analysis for hotel reviews. Proceedings of the Computational Linguistics-Applications (pp. 45– 52). Jacharanka, Poland.
- Korrapati, R. (2017). Hotel reviews from Chennai, India [Data set]. https://www.kaggle.com/ranjitha1/hotel-reviews-city-chennai
- Kumar, A., & Sharan, A. (2020). Deep learning-based frameworks for aspect-based sentiment analysis. In B. Agarwal, R. Nayak, N. Mittal, & S. Patnaik (Eds.), Deep learning-based approaches for sentiment analysis. Algorithms for intelligent systems (pp. 139–158). Springer. https://doi.org/https://doi.org/10.1007/978-981-15-1216-2_6
- Loria, S., & Textblob Contributors. (2020). TextBlob: Simplified text processing (version 0.16.0) [Computer software]. https://textblob.readthedocs.io/en/dev/
- McKinney, W., & Pandas Developer Team. (2019). Pandas: powerful Python data analysis toolkit (version 0.25.0) [Computer software]. https://pandas-docs.github.io/pandas-docs-travis/
- Michal, D. (2016). langdetect (version 1.0.7) [Computer software]. https://pypi.org/project/langdetect/
- Nathania, G., Siautama, R., Claire, A., & Suhartono, D. (2020, November 19–20). Extractive hotel review summarization based on TF/IDF and adjective-noun pairing by considering annual sentiment trends. Proceedings of the 5th International Conference on Computer Science and Computational Intelligence (pp. 558–565), Online, Indonesia. https://doi.org/https://doi.org/10.1016/j.procs.2021.01.040
- Promptcloud. (2018). London-hotels-reviews [Data set]. https://data.world/promptcloud/customer-of-reviews-of-london-basedhotels/file/London_hotel_reviews.csv
- Putra, F. M., Kusumaningrum, R., & Wibowo, A. (2020, November 19–20). Sentiment analysis using Word2vec and long short-term memory (LSTM) for Indonesian hotel reviews. Proceedings of the 5th International Conference on Computer Science and Computational Intelligence (pp.728–735). Online, Indonesia. https://doi.org/https://doi.org/10.1016/j.procs.2021.01.061
- Raut, V. B., & Londhe, D. D. (2014, November 14–16). Opinion mining and summarization of hotel reviews. Proceedings of the Sixth International Conference on Computational Intelligence and Communication Networks (pp. 556–559). Bhopal, India. https://doi.org/https://doi.org/10.1109/CICN.2014.126.
- Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing Journal, 98(2021), 106935. https://doi.org/https://doi.org/10.1016/j.asoc.2020.106935
- Rizka, P. N., Putra, F. M., Kusumaningrum, R., & Wibowo, A. (2019, September 12–13). Word2Vec for Indonesian sentiment analysis towards hotel reviews: An evaluation study. Proceedings of the 4th International Conference on Computer Science and Computational Intelligence (pp. 360–366). Yogyakarta, Indonesia. https://doi.org/https://doi.org/10.1016/j.procs.2019.08.178
- Sann, R., & Lai, P. C. (2020). Understanding homophily of service failure within the hotel guest cycle: Applying NLP-aspect-based sentiment analysis to the hospitality industry. International Journal of Hospitality Management, 91(2020), 102678. https://doi.org/https://doi.org/10.1016/j.ijhm.2020.102678
- Schuckert, M., Liu, X., & Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(2015), 608–621. https://doi.org/https://doi.org/10.1080/10548408.2014.933154
- Sodanil, M. (2016, June 6–8). Multi-language sentiment analysis for hotel reviews. Proceedings of International Conference on Measurement Instrumentation and Electronics (p. 03002). Munich, Germany. https://doi.org/https://doi.org/10.1051/matecconf/20167503002.
- Zvarevashe, K., & Olugbara, O. O. (2018, March 8–9). A framework for sentiment analysis with opinion mining of hotel reviews. Proceedings of the Conference on Information Communications Technology and Society (pp. 1–4). Durban, South Africa. https://doi.org/https://doi.org/10.1109/ICTAS.2018.8368746.