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Exploring Unsupervised Learning Towards Extractive Summarization of User Reviews

Published: 17 October 2017 Publication History

Abstract

Mobile app reviews are important as a crowdsource to improve the quality of these softwares. App stores, which have app reviews, provide a wealth of information derived from users. These information help developers to fix bugs and implement new features desired by users. Despite the reviews usefulness, one of the challenges of application developers is the huge number of reviews published daily, making manual analysis laborious. Hence, the delay in satisfying users may influence the loss of customers. Current researches into this topic have adopted a supervised approach to classify the reviews of the users. In this paper, we used an unsupervised approach to categorize the reviews aiming to generate a summary of the main bugs and new features pointed by users, assisting the application developers to improve the quality their apps. We evaluated the approach empirically and obtained promising results to generate user reviews summaries.

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Cited By

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  • (2022)Aspect-based Sentiment Analysis on Mobile Application Reviews2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTer58063.2022.10024070(183-188)Online publication date: 30-Nov-2022
  • (2020)A close look at socio-technical design features of mobile applications for diabetes self-managementHealth and Technology10.1007/s12553-020-00497-4Online publication date: 26-Oct-2020
  • (2019)Characterization of the discrepancies between scores and texts of movie reviewsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360296(229-236)Online publication date: 29-Oct-2019
  • Show More Cited By

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cover image ACM Other conferences
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web
October 2017
522 pages
ISBN:9781450350969
DOI:10.1145/3126858
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • SBC: Brazilian Computer Society
  • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
  • CGIBR: Comite Gestor da Internet no Brazil
  • CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Author Tags

  1. clustering
  2. text mining
  3. user review

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  • Short-paper

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Webmedia '17
Sponsor:
  • SBC
  • CNPq
  • CGIBR
  • CAPES
Webmedia '17: Brazilian Symposium on Multimedia and the Web
October 17 - 20, 2017
RS, Gramado, Brazil

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WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;
Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all
  • (2022)Aspect-based Sentiment Analysis on Mobile Application Reviews2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTer58063.2022.10024070(183-188)Online publication date: 30-Nov-2022
  • (2020)A close look at socio-technical design features of mobile applications for diabetes self-managementHealth and Technology10.1007/s12553-020-00497-4Online publication date: 26-Oct-2020
  • (2019)Characterization of the discrepancies between scores and texts of movie reviewsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360296(229-236)Online publication date: 29-Oct-2019
  • (2018)Using Supervised Classification to Detect Political Tweets with Political ContentProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243113(245-252)Online publication date: 16-Oct-2018
  • (2018)Future Internet and Scalability Techniques in Mobile CrowdsourcingProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243085(77-84)Online publication date: 16-Oct-2018

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