Sorting Things Out: Classification and Its Consequences.

Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

The usefulness of mobile devices has increased greatly in recent years allowing users to perform more tasks in a mobile context. This increase in usefulness has come at the expense of the usability of these devices in some contexts. We conducted a small review of mobile usability models and found that usability is usually measured in terms of three attributes; effectiveness, efficiency and satisfaction. Other attributes, such as cognitive load, tend to be overlooked in the usability models that are most prominent despite their likely impact on the success or failure of an application. To remedy this we introduces the PACMAD (People At the Centre of Mobile Application Development) usability model which was designed to address the limitations of existing usability models when applied to mobile devices. PACMAD brings together significant attributes from different usability models in order to create a more comprehensive model. None of the attributes that it includes are new, but the existing prominent usability models ignore one or more of them. This could lead to an incomplete usability evaluation. We performed a literature search to compile a collection of studies that evaluate mobile applications and then evaluated the studies using our model.

/pdf/usability-of-mobile-applications-literature-review-and-2sgkvggy18.pdf

Usability of mobile applications: literature review and rationale for a new usability model

App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.

/pdf/how-do-users-like-this-feature-a-fine-grained-sentiment-4el59000r2.pdf

How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3323&context=sis_research

AR-miner: mining informative reviews for developers from mobile app marketplace

Mobile app reviews are valuable repositories of ideas coming directly from app users. Such ideas span various topics, and in this paper we show that 23.3% of them represent feature requests, i.e. comments through which users either suggest new features for an app or express preferences for the re-design of already existing features of an app. One of the challenges app developers face when trying to make use of such feedback is the massive amount of available reviews. This makes it difficult to identify specific topics and recurring trends across reviews. Through this work, we aim to support such processes by designing MARA (Mobile App Review Analyzer), a prototype for automatic retrieval of mobile app feature requests from online reviews. The design of the prototype is a) informed by an investigation of the ways users express feature requests through reviews, b) developed around a set of pre-defined linguistic rules, and c) evaluated on a large sample of online reviews. The results of the evaluation were further analyzed using Latent Dirichlet Allocation for identifying common topics across feature requests, and the results of this analysis are reported in this paper.

Retrieving and analyzing mobile apps feature requests from online reviews

In this paper, we explore the content of online reviews of mobile applications to get a better understanding of the most recurring issues users report through reviews, and the way the price and the rating of an app influences the type and the amount of feedback users report. Results show that users tend to provide positive feedback, often associating it with requirements for additional features. Also, users tend to provide more feedback for the lower rated apps and the optimal price range was found to be between £2.25 and £3.50.

What are you complaining about?: a study of online reviews of mobile applications

This paper introduces a framework for developing mobile apps. The framework relies heavily on app stores and, particularly, on online reviews from app users. The underlying idea is that app stores are proxies for users because they contain direct feedback from them. Such feedback includes feature requests and bug reports, which facilitate design and testing respectively. The framework is supported by MARA, a prototype system designed to automatically extract relevant information from online reviews.

/pdf/online-reviews-as-first-class-artifacts-in-mobile-app-6zasq1fm90.pdf

Online Reviews as First Class Artifacts in Mobile App Development

The power of mobile devices has increased dramatically in the last few years. These devices are becoming more sophisticated and allow users to accomplish a wide variety of tasks while on the move. The ease with which mobile apps can be created and distributed has resulted in a number of usability issues becoming more prevalent. This paper describes the range of usability issues encountered at all stages of the mobile app life cycle, from when users begin to search for an app to when they finally remove the app from their device. Using these results the authors developed a number of guidelines for both app developers and app platform developers that will improve the overall usability of mobile apps.

Evaluating Mobile Applications: A Spreadsheet Case Study

https://researchportal.port.ac.uk/portal/files/15149801/Exploring_the_gap.pdf

Claudia Iacob

Papers

Retrieving and analyzing mobile apps feature requests from online reviews

What are you complaining about?: a study of online reviews of mobile applications

Online Reviews as First Class Artifacts in Mobile App Development

Evaluating Mobile Applications: A Spreadsheet Case Study

Exploring the gap between the student expectations and the reality of teamwork in undergraduate software engineering group projects