Extracting Service Aspects from Web Reviews

Hao, Jinmei; Li, Suke; Zhong, Chen

doi:10.1007/978-3-642-16515-3_40

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Within tourism holiday reviews, experiences and opinions about services are published on several holiday review websites such as TripAdvisor, Expedia and http://CruiseCritic.com. Using such holiday review websites as data sources is a growing area of interest for marketers and researchers alike (Sohns and Breitner, ; Andreassen and Streukens, ; Hao et al ., ; Stringam and Gerdes, ). Interestingly, most papers using holiday review websites as data sources are published by researchers from the domain of computer and information sciences and tourism.…”

Section: Literature Reviewmentioning

confidence: 99%

An Exploratory Use of Web Content Analysis to Understand Cruise Tourism Services

Brejla¹,

Gilbert

2012

Int. J. Tourism Res.

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Research within the cruise industry now has the ability to utilize Web-based content analysis. This paper provides an example of a methodology that explores patterns of co-creation of value on board cruise ships using holiday reviews retrieved through Web content mining from CruiseCritic.com. The aim is to add to the field of co-creation and cruise tourism by providing an approach to exploratory Web-based data collection. With the use of a data-driven approach to knowledge discovery, this paper provides a means to achieve a deeper understanding of guest-to-guest and guest-to-staff interactions on board cruise ships.Source: Literature review and convenience sample of 5 reviews taken from the CruiseCritic.com data set.

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Section: Literature Reviewmentioning

confidence: 99%

An Exploratory Use of Web Content Analysis to Understand Cruise Tourism Services

Brejla¹,

Gilbert

2012

Int. J. Tourism Res.

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“…One problem is that limited documents hinder identifying changing NFRs in a timely manner. Hao et al utilize machine learning techniques to extract the aspects of service quality from Web reviews for conducting automatic service quality evaluation [8]. Carreño et al adapt information retrieval techniques including topic modeling for exploring the rich user comments of mobile applications to extract new/changed requirements for future releases [9].…”

Section: Related Workmentioning

confidence: 99%

“…However, these approaches mostly rely on manual content analysis and as such, are not efficient for dealing with large amounts of online reviews in order to shorten time-to-market. Obviously, automated techniques, such as text mining, information retrieval, and machine learning can be effective tools for identifying software features and associated opinions mentioned in user comments [8][9]. However, due to complex and diverse opinion expressions, it is challenging to utilize automated analysis for accurately deriving constructive feedback from the reviews of software systems.…”

Section: Introductionmentioning

confidence: 99%

For User-Driven Software Evolution: Requirements Elicitation Derived from Mining Online Reviews

Jiang

Ruan

Zhang

et al. 2014

Advances in Knowledge Discovery and Data Mining

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Abstract. Online reviews that manifest user feedback have become an available resource for eliciting requirements to design future releases. However, due to complex and diverse opinion expressions, it is challenging to utilize automated analysis for deriving constructive feedback from these reviews. What's more, determining important changes in requirements based on user feedback is also challenging. To address these two problems, this paper proposes a systematic approach for transforming online reviews to evolutionary requirements. According to the characteristics of reviews, we first adapt opinion mining techniques to automatically extract opinion expressions about common software features. To provide meaningful feedback, we then present an optimized method of clustering opinion expressions in terms of a macro network topology. Based on this feedback, we finally combine user satisfaction analysis with the inherent economic attributes associated with the software's revenue to determine evolutionary requirements. Experimental results show that our approach achieves good performance for obtaining constructive feedback even with large amounts of review data, and furthermore discovers the evolutionary requirements that tend to be ignored by developers from a technology perspective.

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“…For service oriented development, the users can be approached through their ‗voice' from online resources and their feedback can be analyzed to elicit the require information. In recent years, there has been a substantial body of research for proposing methods, tools and techniques on collecting and analyzing online users' feedback, comments and review for extracting useful information [11][12][13][14][15][16][17] (e.g. data mining, information retrieval, crowd sourcing, parsing, natural language processing (NLP), sentiment analysis).…”

Section: User Feedback Analysismentioning

confidence: 99%

Users' voice and service selection: An empirical study

Bano

Zowghi

2014

2014 IEEE 4th International Workshop on Empirical Requirements Engineering (EmpiRE)

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Service Oriented software development saves time by reusing existing services and integrates them to create a new system. But selecting a service that satisfies the requirements of all concerned stakeholders is a challenging task. The situation has been exacerbated within the past few years with huge number of services available that offer similar functionalities where the analysts require additional information for making better decision for service selection. User feedback analysis has recently gained a lot of attention for its potential benefits in various areas of requirements engineering. The aim of this research is to evaluate the impact of feedback provided by the end users of the services, on the decision making process for the service selection. In this paper we present an empirical study that utilizes user feedback analysis for selection of a service among 92 available services with similar functionalities. The results show that in scenarios with significant number of services, it is helpful for analysts to consider additional information to select optimally best matched service to the requirements.

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Extracting Service Aspects from Web Reviews

Cited by 8 publications

References 6 publications

An Exploratory Use of Web Content Analysis to Understand Cruise Tourism Services

An Exploratory Use of Web Content Analysis to Understand Cruise Tourism Services

For User-Driven Software Evolution: Requirements Elicitation Derived from Mining Online Reviews

Users' voice and service selection: An empirical study

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