Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

被引:11
作者
Alotaibi, Bashayer [1 ]
Abbasi, Rabeeh Ayaz [2 ]
Aslam, Muhammad Ahtisham [1 ]
Saeedi, Kawther [1 ]
Alahmadi, Dimah [1 ]
机构
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia
[2] Quaid I Azam Univ, Dept Comp Sci, Islamabad 45320, Pakistan
关键词
Data mining; machine learning; sentiment analysis; startups; entrepreneurship; Twitter; SENTIMENT ANALYSIS; POWER;
D O I
10.1109/ACCESS.2020.2965181
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-a-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.
引用
收藏
页码:10718 / 10730
页数:13
相关论文
共 38 条
  • [1] Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination
    Abbasi, Rabeeh Ayaz
    Maqbool, Onaiza
    Mushtaq, Mubashar
    Aljohani, Naif R.
    Daud, Ali
    Alowibdi, Jalal S.
    Shahzad, Basit
    [J]. TELEMATICS AND INFORMATICS, 2018, 35 (04) : 892 - 912
  • [2] Abo MEM, 2018, 2018 INT C COMP CONT, P1
  • [3] AlSheikh S.S., 2017, 2017 INT C BEHAV EC, P1, DOI [DOI 10.1109/BESC.2017.8256364, 10.1109/besc.2017, DOI 10.1109/BESC.2017]
  • [4] [Anonymous], 2018, INT C INFORM COMMUNI
  • [5] [Anonymous], 2016, Int J Comput Appl, DOI [DOI 10.5120/IJCA2016908328, 10.5120/ijca2016908328]
  • [6] Antretter T., 2018, P INT C INF SYST ICI
  • [7] Bhuta S, 2014, PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON ISSUES AND CHALLENGES IN INTELLIGENT COMPUTING TECHNIQUES (ICICT), P583, DOI 10.1109/ICICICT.2014.6781346
  • [8] Subjective Text Mining for Arabic Social Media
    Bin Hathlian, Nourah F.
    Hafez, Alaaeldin M.
    [J]. INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS, 2017, 13 (02) : 1 - 13
  • [9] How CEOs use Twitter: A comparative analysis of Global and Latin American companies
    Capriotti, Paul
    Ruesja, Laura
    [J]. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2018, 39 : 242 - 248
  • [10] A study of the effects of preprocessing strategies on sentiment analysis for Arabic text
    Duwairi, Rehab
    El-Orfali, Mahmoud
    [J]. JOURNAL OF INFORMATION SCIENCE, 2014, 40 (04) : 501 - 513