A preliminary analysis of vocabulary in mobile app user reviews

Leonard Hoon, Rajesh Vasa, Jean-Guy Schneider, Kon Mouzakis

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

42 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 24th Australian Computer-Human Interaction Conference, OzCHI 2012
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Print)9781450314381
Publication statusPublished - 2012
Externally publishedYes
EventAustralian Computer-Human Interaction Conference 2012 - Melbourne, Australia
Duration: 26 Nov 201230 Nov 2012
Conference number: 24th
https://dl.acm.org/doi/10.1145/2414536.2414578 (Proceedings)


ConferenceAustralian Computer-Human Interaction Conference 2012
Abbreviated titleOzCHI 2012
Internet address


  • mobile apps
  • rating systems
  • reviews
  • text mining
  • user expectations
  • user issues
  • user vocabulary
  • word-of-mouth

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