Kon Mouzakis

A preliminary analysis of mobile app user reviews

The advent of online software distribution channels like Apple Inc.s App Store and Google Inc.s Google Play has offered developers a single, low cost, and powerful distribution mechanism. These online stores help users discover apps as well as leave a review. Ratings and reviews add value to both the developer and potential new users by providing a crowd-sourced indicator of app quality. Hence, for developers it is important to get positive reviews and high ratings to ensure that an app has a viable future. But, what exactly do users say on these app stores? And more importantly, what is the experience that compels a user to leave either a positive or a negative rating? Our analysis of 8.7 million reviews from 17,330 apps shows that users tend to leave short, yet informative reviews, and the rating as well as the category influences the length of a review. In this preliminary study, we found that users will leave longer messages when they rate an app poorly, and the depth of feedback in certain categories is significantly higher than for other.

A preliminary analysis of vocabulary in mobile app user reviews

Online software distribution channels such as Apple Inc.s App Store and Google Inc.s Google Play provide a platform for third-party app distribution. These online stores feature a public review system, allowing users to express opinions regarding purchased apps. These reviews can influence product-purchasing decisions via polarised sentiment (1 to 5 stars) and user expressed opinion. For developers, reviews are a user-facing crowd-sourced indicator of app quality. Hence, high ratings and positive reviews affect the viability of an apps commercial feasibility. However, it is less clear what information is contained within these reviews, and more importantly, if an analysis of these reviews can inform developers of design priorities as opposed to just influencing purchasing decisions. We analysed 8.7 million reviews from 17,330 apps on the App Store and found that the most frequently used words in user reviews lean toward expressions of sentiment despite employment of only approximately 37% of the words within the English language dictionary. Furthermore, the range of words used to express negative opinions is significantly higher than when positive sentiments are expressed.

Awesome!: conveying satisfaction on the app store

In a competitive market like the App Store, high user perceived quality is paramount, especially due to the public review system offered. These reviews give developers feedback on their own apps, as well as help provide data for competitor analysis. However, given the size of the data set, manual analysis of reviews is unrealistic, especially given the need for a rapid response to changing market dynamics. Current research into mobile app reviews has provided an insight into the statistical distributions, but there is minimal knowledge about the content in these reviews. In particular, we do not know if the aggregated numerical rating is a reliable indicator of the information within a review. This work reports on an analysis of reviews to determine how closely aligned the numerical ratings are to the textual description. We observed that short user reviews mostly contain a small set of words, and the corresponding numerical rating matches the underlying sentiment.

Courtroom evidence presentation technology: overcoming traditional barriers

The Supreme Court of Victoria is an heritage listed building steeped in tradition and reticent to physical change. The current capability of the courtrooms to exhibit advanced technology derived evidence is substantially inadequate. This paper discusses the negative environmental impact, the inadequacies of courtroom facilities and inconsistencies in comparison to the available evidence and the requirements of jurors and courts in relation to evidence presentation. It also discusses the issues, possibilities and limitations involved in the implementation of an IT solution to the presentation of evidence.

Supporting diverse challenges of ageing with digital enhanced living solutions

By the 2050, it is estimated that the proportion of people over the age of 80 will have risen from 3.9% to 9.1% of population of Organisation for Economic Cooperation and Development countries. A large proportion of these people will need significant help to manage various chronic illnesses, including dementia, heart disease, diabetes, limited physical movement and many others. Current approaches typically focus on acute episodes of illness and are not well designed to provide adequately for daily living care support. In our rapidly ageing society, a critical need exists for effective, affordable, scalable and safe in-home and in-residential care solutions leveraging a range of current and emerging sensor, interaction and integration technologies. Key aims are to support the ageing to live longer in their own homes; make daily challenges associated with ageing less limiting through use of technology supports; better support carers - both professional and family - in providing monitoring, proactive intervention, and community connectedness; enable in-home and in-residential care organisations to scale their support services and better use their workforces; and ultimately provide better quality of life. Deakin University researchers have been investigating a range of emerging technologies and platforms to realise this vision, which we in broad terms coin Digital Enhanced Living, in the ageing space but also supporting those with anxiety and depression, sleep disorders, various chronic diseases, recovery from injury, and various predictive analytics. A Smart Home solution, carried out in conjunction with a local start-up, has produced and trialled a novel sensor, interaction, and AI-based technology. Virtual Reality (VR) solutions have been used to support carers in the set-up of dementia-friendly homes, in conjunction with Alzheimers Australia. Activity and nutrition solutions, including the use of conversational agents, have been used to build dialogue to engage and change behaviour. Predictive analytics, in conjunction with major hospitals, have been applied to large medical datasets to better support professionals making judgements around discharge outcomes. A set of lessons have been learned from the design, deployment and trialing of these diverse solutions and new development approaches have been crafted to address the challenges faced. In particular, we found that there is a need to consider user emotional expectations as first-class citizens and create methodologies that consider the user needs during the creation of the software solutions. We find that quality and emotional aspects have to be engineered into the solution, rather than added after a technical solution is deployed.

Recoding low-level simulator data into a record of meaningful task performance: The integrated task modeling environment (ITME)

The Integrated Task Modeling Environment (ITME) is a user-friendly software tool that has been developed to automatically recode low-level data into an empirical record of meaningful task performance. The present research investigated and validated the performance of the ITME software package by conducting complex simulation missions and comparing the task analyses produced by ITME with task analyses produced by experienced video analysts. A very high interrater reliability (≥0.94) existed between experienced video analysts and the ITME for the task analyses produced for each mission. The mean session time:analysis time ratio was 1:24 using video analysis techniques and 1:5 using the ITME. It was concluded that the ITME produced task analyses that were as reliable as those produced by experienced video analysts, and significantly reduced the time cost associated with these analyses.

Mobile pen-based technologies for drivers licence administration

One of the major advantages of mobile computing devices is that they facilitate data capture when the operator is on the move. The data collected can be readily processed for analysis and reporting purposes, without the need for manually transcribing the data into an electronic form. This paper describes the design and development of two prototypical systems using mobile pen-based technologies for the administration of learner drivers licence testing. We highlight some of the design issues and report the lessons learned.