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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/238314
- A preliminary analysis of vocabulary in mobile app user reviews
- Hoon, Leonard; Vasa, Rajesh; Schneider, Jean-Guy; Mouzakis, Kon
- 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 app's 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.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Faculty of Information and Communication Technologies
- Proceedings of 'Integration, Interaction, Innovation, Immersion, Inclusion', the Annual Conference of the Australian Computer-Human Interaction Special Interest Group (OZCHI 2012), Melbourne, Victoria, Australia, 26-30 November 2012, pp. 245-248
- Publication year
- Mobile applications; Mobile apps; Rating systems; Reviews; Text mining; User expectations; User issues; User reviews; User vocabulary; Vocabulary; Word-of-mouth
- 9781450314381, 1450314384
- Publisher URL
- Copyright © 2012 ACM.
- Peer reviewed