Analysis and Application of Tourists’ Sentiment Based on Hotel Comment Data

Wu, Bin; Bo, Feng; Du, Jiulin; Huang, Xiaojia; Gao, Liangliang

doi:10.1109/cost57098.2022.00080

Cited by 1 publication

(1 citation statement)

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“…Li et al collected data such as Beijing hotels' names, addresses, and operating hours with this tool [43]. Similarly, Wu et al extracted user reviews for Lanzhou hotels using Bazhuayu and conducted a sentiment analysis on the data [44]. Typically, it is necessary to perform preprocessing steps after acquiring Chinese text data, like removing irrelevant symbols and stop words, as well as segmenting Chinese text.…”

Section: Online Review Acquisition Toolsmentioning

confidence: 99%

Construction of Product Appearance Kansei Evaluation Model Based on Online Reviews and FAHP: A Case Study of Household Portable Air Conditioners

Du,

Zhang,

Cai

et al. 2024

Sustainability

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Meeting the personalized needs of users is the key to achieving the sustainable success of a product. It depends not only on the product’s functionality but also on satisfying users’ emotional needs for the product’s appearance. Therefore, researchers have been conducting research focusing on Kansei engineering theory to determine users’ emotional needs effectively. The initial process involves accurately extracting and filtering emotional data and Kansei words from consumers. Thus, we propose an evaluation model to efficiently obtain, screen, and sort these Kansei words based on Kansei engineering, using household portable air conditioners as research subjects. By integrating techniques for online user comment mining methods, users’ Kansei terms related to the product’s appearance can be gathered efficiently. These terms are then combined with image samples and filtered to determine a final set of 16 Kansei word pairs. Subsequently, the fuzzy analytic hierarchy process (FAHP) is utilized to prioritize these terms, and the fuzzy comprehensive evaluation (FCE) method is used to validate the results and determine the applicability of the evaluation model. The results showed that Kansei words could be quickly and objectively acquired using existing text mining techniques on online reviews. Moreover, the weights of different Kansei terms of the product’s appearance in the consumer’s perception are accurately produced through the FAHP. This evaluation model marks a significant advancement in accurately obtaining users’ emotional data in Kansei engineering. It offers valuable guidance for designing products that meet users’ personalized needs, enhancing design efficiency and reducing resource wastage at the early stages of designing, and improving the sustainability development of Kansei engineering.

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Section: Online Review Acquisition Toolsmentioning

confidence: 99%