DC FieldValueLanguage
dc.contributor.authorGiannakopoulos, Georgios-
dc.contributor.authorDrivas, Ioannis-
dc.contributor.authorTriantafyllou, Ioannis-
dc.date.accessioned2023-10-12T20:06:57Z-
dc.date.available2023-10-12T20:06:57Z-
dc.date.issued2020-11-17-
dc.identifier.issn10994300-
dc.identifier.other85096380813-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/235-
dc.description.abstractAcquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofEntropyen_US
dc.subjectApp business strategyen_US
dc.subjectApp reviewsen_US
dc.subjectFeature extraction methodsen_US
dc.subjectMachine learning methodsen_US
dc.subjectReviews classificationen_US
dc.subjectText analysisen_US
dc.subjectText classificationen_US
dc.subjectTopics extractionen_US
dc.titleHow to utilize my app reviews? A novel topics extraction machine learning schema for strategic business purposesen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/e22111310en_US
dc.identifier.scopus2-s2.0-85096380813-
dc.relation.deptDepartment of Archival, Library and Information Studiesen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume22en_US
dc.relation.issue11en_US
dc.identifier.spage1en_US
dc.identifier.epage21en_US
dc.linkhttps://www.mdpi.com/1099-4300/22/11/1310en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldSocial Sciencesen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-1659-3504-
crisitem.author.orcid0000-0003-2407-9502-
crisitem.author.orcid0000-0001-5273-0855-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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