Computationally effective algorithm for information extraction and online review mining

Author(s):  
Boris Kraychev ◽  
Ivan Koychev
2015 ◽  
Vol 17 (3) ◽  
pp. 95-111
Author(s):  
Seung-Yean Cho ◽  
◽  
Jee-Eun Choi ◽  
Kyu-Hyun Lee ◽  
Hee-Woong Kim

2017 ◽  
Vol 43 (6) ◽  
pp. 435-450
Author(s):  
Inki Yoo ◽  
Jungju Park ◽  
Jeonghoon Mo
Keyword(s):  

Author(s):  
Ke Ma ◽  
Beibei Jiang

Abstract In this paper, we will identify the destination attributes of a popular urban park and investigate their specific roles in forming visitors' behavioural intentions using text mining approaches. The principles of natural language processing and psychometric procedure were combined to achieve the objectives of the research. Initially, park visitors’ online reviews were collected and analysed to identify possible latent dimensions for questionnaire design. Then, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used for crucial factor selection and verification. Lastly, a structural equation model (SEM) was constructed to investigate the impacts of these park attributes on the behavioural intention of visitors.


2019 ◽  
Vol 53 (1) ◽  
pp. 33-57 ◽  
Author(s):  
Mu-Chen Chen ◽  
Yu-Hsiang Hsiao ◽  
Kuo-Chien Chang ◽  
Ming-Ke Lin

Purpose Leisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining. Design/methodology/approach The online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships. Findings The results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results. Originality/value Now, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.


2015 ◽  
Vol 7 (1) ◽  
pp. 49-53
Author(s):  
Xixiang Sun ◽  
Xiaoqing Song ◽  
Xiangdong Liu
Keyword(s):  

2019 ◽  
Vol 8 (4) ◽  
pp. 2151-2153

Customer comments form an integral part for identification of failures and success of a product. Buying patterns of a customer greatly depends on the pattern of comments posted online. Online review/comments can be broadly classified into positive, negative and neutral. Many tools available in market can be used for their classification. However, there are various flaws in classifying methods that can tweak the result of these comments such as “Unidentified/Hidden information in neutral comments”, “Wrong keyword extraction while splitting words”, “fake comments based on frequency of duplicate comment or reviewer”. This paper addresses this problem based on online product comments posted on Amazon website and proposes an ideal flow chart and algorithm to address these problems.


2014 ◽  
Vol 16 (3) ◽  
pp. 113-134 ◽  
Author(s):  
Seung Yeon Cho ◽  
◽  
Hyun-Koo Kim ◽  
Beomsoo Kim ◽  
Hee-Woong Kim
Keyword(s):  

2016 ◽  
Vol 107 (3) ◽  
pp. 1435-1455 ◽  
Author(s):  
Qingqing Zhou ◽  
Chengzhi Zhang ◽  
Star X. Zhao ◽  
Bikun Chen
Keyword(s):  

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