A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

被引:33
|
作者
Ramon Saura, Jose [1 ]
Bennett, Dag R. [2 ]
机构
[1] Rey Juan Carlos Univ, Fac Social Sci & Law, Dept Business Econ, Paseo Artilleros S-N, Madrid 28032, Spain
[2] London South Bank Univ, Ehrenberg Ctr Res Mkt, 103 Borough Rd, London SE1 0AA, England
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 04期
关键词
data text mining; sentiment analysis; business intelligence; marketing intelligence; RELIABILITY;
D O I
10.3390/sym11040519
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence (BI) and marketing intelligence (MI) that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments. In this context, the present study proposes a three-step research methodology based on data text mining (DTM). In further research, this methodology can be used for business intelligence analysis (BIA) strategies to analyze user generated content (UGC) in social networks and on digital platforms. The proposed methodology unfolds in the following three stages. First, a Latent Dirichlet Allocation (LDA) model that determines the database topic is used. Second, a sentiment analysis (SA) is proposed. This SA is applied to the LDA results to divide the topics identified in the sample into three sentiments. Thirdly, textual analysis (TA) with data text mining techniques is applied on the topics in each sentiment. The proposed methodology offers important advances in data text mining in terms of accuracy, reliability and insight generation for both researchers and practitioners seeking to improve the BIA processes in business and other sectors.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Business Intelligence, Data Mining, and Future Trends
    Boland, Giles W.
    Thrall, James H.
    Duszak, Richard, Jr.
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2015, 12 (01) : 9 - 11
  • [42] Visual data mining for business intelligence applications
    Hao, M
    Dayal, U
    Hsu, M
    [J]. WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS, 2000, 1846 : 3 - 14
  • [43] Mining clusters and corresponding interpretable descriptions - a three-stage approach
    Drobics, M
    Bodenhofer, U
    Winiwarter, W
    [J]. EXPERT SYSTEMS, 2002, 19 (04) : 224 - 234
  • [44] Data mining business intelligence for competitive advantage
    Gupta, RK
    [J]. ELECTRONICS INFORMATION & PLANNING, 2001, 29 (01): : 27 - 42
  • [45] Efficiency Measurement With A Three-Stage Hybrid Method
    Ertugrul, Irfan
    Oztas, Tayfun
    [J]. INTERNATIONAL JOURNAL OF ASSESSMENT TOOLS IN EDUCATION, 2018, 5 (02): : 370 - 388
  • [46] The Three-Stage Evolution of Full Cost Accounting in Business Economics
    Santini, Fabio
    [J]. BUSINESS PERFORMANCE MEASUREMENT AND MANAGEMENT: NEW CONTEXTS, THEMES AND CHALLENGES, 2010, : 251 - 266
  • [47] Using text mining tools for event data analysis
    Stathopoulou, T
    [J]. Knowledge Mining, 2005, 185 : 239 - 253
  • [48] Industrial eco-efficiency in China: A provincial quantification using three-stage data envelopment analysis
    Zhang, Jiangxue
    Liu, Yimeng
    Chang, Yuan
    Zhang, Lixiao
    [J]. JOURNAL OF CLEANER PRODUCTION, 2017, 143 : 238 - 249
  • [49] Are big data talents different from business intelligence expertise? Evidence from text mining using job recruitment advertisements
    Wu, Jun
    Shi, Honglei
    Yang, Jiaping
    [J]. 2017 14TH INTERNATIONAL CONFERENCE ON SERVICES SYSTEMS AND SERVICES MANAGEMENT (ICSSSM), 2017,
  • [50] Using Fuzzy Sets in a Data-to-Text System for Business Service Intelligence
    Ramos-Soto, A.
    Janeiro, J.
    Alonso, J. M.
    Bugarin, A.
    Berea-Cabaleiro, D.
    [J]. ADVANCES IN FUZZY LOGIC AND TECHNOLOGY 2017, VOL 3, 2018, 643 : 220 - 231