Analyzing Visitors’ Review of Homestays Located in Nature-Based Settings: An NLP Based Approach

Received: 16th October 2021 | Review: 4th December 2021 | Accepted: 12th Feb 2022

Volume 30, Issue-2, April 2022

Purpose: Sentiment analysis techniques such as Natural Language Processing(NLP) provide a powerful tool to analyze textual data. Along with machine learning and other big data methods, these techniques are used in improving customer service quality in different sectors. This paper utilizes sentiment analysis techniques to identify key themes surrounding visitors’ homestay experience in nature-based settings.

Methodology:Analysis of 2369 TripAdvisor reviews through Structural Topic Modeling (STM) reveals how high rated homestay experiences differ from those rated low on various parameters

Findings: The paper contributes to the knowledge on<text mining & its application in improving customer service in the hospitality &tourism domain. The research has practical usage for homestay stakeholders and future direction for further research.

Key words: Sentiment Analysis, Text Analysis, TripAdvisor, Topic Modeling,Homestay, Customer Review

Download View

References :

Chakraborty, B. (2019, June). Homestay and women empowerment: A case study of women managed tourism product in kasar devi, uttarakhand, india. In TISC-Tourism International Scientific Conference Vrnjačka Banja , Vol. 4, No. 1, pp. 202-216.
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
García, A., Gaines, S., & Linaza, M. T. (2012). A lexicon based sentiment analysis retrieval system for tourism domain. Expert Syst Appl Int J, 39(10), 9166-9180.
Goodwin, H., & Santilli, R. (2009). Community-based tourism: A success. ICRT Occasional paper, 11(1), 37.
Gräbner, D., Zanker, M., Fliedl, G., & Fuchs, M. (2012, January). Classification of customer reviews based on sentiment analysis. In ENTER, pp. 460-470.
Gretzel, U., Yoo, K. H., & Purifoy, M. (2007). Online travel review study: Role and impact of online travel reviews.
Kasper, W., & Vela, M. (2011, October). Sentiment analysis for hotel reviews. In Computational linguistics-applications conference , Vol. 231527, pp. 45-52.
Macek, I. C. (2013). Homestays as Livelihood Strategies in Rural Economies: The case of Johar Valley, Uttarakhand, India [Masters dissertation, University of Washington].
Misner, I. R. (1994). The world’s best-known marketing secret: building your business with word-of-mouth marketing. Bard.
Oranratmanee, R. (2011). Re-utilizing space : accommodating tourists in homestay houses in northern thailand. Jars, 8(1), 35–54.

Price, Martin F. (1992). Patterns of the development of tourism in mountain environments. GeoJournal, Vol. 27(1), Mountain Environments, 87-96.
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, 98, 106935.
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O’Reilly Media, Inc. Thapa, B., Manavi, A. D. , & Malini, D. H. (2018). Unraveling tourists’ preferred homestay attributes from online reviews: A sentiment analysis approach. International Journal of Pure and Applied Mathematics, Vol. 119(15), 1567-1585.