Opinion Mining in Tourism

Author:

Özen Ibrahim Akın1ORCID,Ilhan Ibrahim1ORCID

Affiliation:

1. Faculty of Tourism, Nevşehir Hacı Bektas Veli University, Turkey

Abstract

In the tourism sector, online tourist reviews analysis is one of the methods to evaluate the products and services offered by businesses and understand the needs of tourists. These reviews take place in social networks and e-commerce sites in parallel with the developments in information and communication technologies. Tourists generate these reviews during or after their use of the products or services. In the literature, these reviews are referred to as UGC (User Generated Content) or eWOM (electronic word-of-mouth). The scientific evaluation of the textual contents in tourist reviews is done by text mining, which is a sub-area of data mining. This chapter discusses the methods and techniques of opinion mining or sentiment analysis. In addition, aspect-based sentiment analysis and techniques to be used in the application are discussed. A case study was carried out using aspect-based sentiment analysis method. In the application “Cappadocia home cooking” restaurant used tourist reviews.

Publisher

IGI Global

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Aspect-Based Sentiment Analysis on Social Media Comments (Twitter) : The Attributes of Service Robots in the Hotel and Restaurant Industry;Journal of Quality Assurance in Hospitality & Tourism;2024-08-09

2. An Integrated Framework for Automated Web Scraping and Sentiment Analysis of Product Reviews;Journal of Machine Intelligence and Data Science;2024

3. Convolutional Neural Networks- Bidirectional Long Short Term Memory Based Fake Review Classification;2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS);2022-05-25

4. Classification of Coronavirus Disease with Artificial Intelligence and Machine Learning;European Journal of Science and Technology;2022-04-11

5. Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification;Concurrency and Computation: Practice and Experience;2021-08-17

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