调查 Google-Play 应用程序标题对成功的影响

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-02-21 DOI:10.1016/j.bdr.2024.100443
Ahmad Bilal , Hamid Turab Mirza , Ibrar Hussain , Adnan Ahmad
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引用次数: 0

摘要

标题(名称)是与移动(智能手机)应用程序相关的主要信息,因为它描述了应用程序的功能和服务。一个醒目的标题可以吸引客户选择某个应用程序而不是其他应用程序。应用程序开发公司非常清楚这一现象,并投入大量精力,用引人注目的关键词、短语和主题来制作应用程序标题,以追求更高的安装率。然而,据我们所知,研究应用程序标题对成功的影响的传统文献非常有限。使用科学(数据分析)方法研究应用程序标题的例子可能屈指可数。此外,这些对标题的研究都是以监督学习为主,而传统文献可能缺乏任何无监督(聚类)数据分析技术来衡量标题对应用程序成功的影响。因此,本研究工作提出了一种基于多层和算法的无监督数据分析方法。初始层对应用程序标题进行聚类,后续层从这些聚类中提取各种文本特征,最后一层对提取的属性进行细化。一般来说,标题中的某些文本特征被证明与应用程序的安装有正反两方面的联系。对结果的验证证实,这种建议的方法可以成功地从应用程序标题(文本数据)中检测出与成功相关的最突出特征。
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Investigating Influence of Google-Play Application Titles on Success

The title (name) is the primary information related to a mobile (smartphone) application, as it describes its functions and services. An eye-catching title can entice customers to choose a certain application over others. Application development companies are well aware of this phenomenon and invest significant efforts in crafting their application titles with compelling keywords, phrases and topics in pursuit of higher installs. However, to the best of our knowledge, traditional literature that investigates the impact of application titles on success is limited. There may be only a few instances where scientific (data-analytical) approaches have been used to examine application titles. Moreover, these investigations of titles are dominated by supervised learning and traditional literature may lack any unsupervised (cluster) data analysis techniques to measure the impact of titles on application success. Therefore, this research work proposes an unsupervised data analysis approach based on multiple layers and algorithms. The initial layer clusters the application titles, the subsequent layer extracts various textual features from these clusters and the final layer refines the extracted attributes. In general, certain textual features in the titles are proven to be positively and negatively linked with the application installs. Verification of the results has confirmed that this proposed approach can successfully detect the most prominent features from application titles (textual data) that correlate with success.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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