Detecting Syriatel`s Success Indicators Using Textual Data Mining for Arabic Language

Hanan Mohsen Wassouf¹ , Mohammad-Bassam Kurdy²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.3 , pp.28-32, Sep-2021

Online published on Sep 30, 2021

Copyright © Hanan Mohsen Wassouf, Mohammad-Bassam Kurdy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Despite the broad knowledge and skills of the millennial generation, recent studies indicate that 90% of Companies are classified as failing companies, and usually end in bankruptcy due to many factors, including a departure from the market need, as the percentage of companies that built their activity around processing a good idea, but far from the needs of the market to bankruptcy 42% of the total companies. In addition to others reasons like the lack of liquidity and incomes with less capital than necessary, the weakness of the founding team and the emergence of many competing companies. All these reasons lead us to think about studying and identifying the success factors for these emerging companies in order to mitigate the possibility of failure. The target study will be Syriatel Mobile Telecom company which is most famous telecom company in Syria.

Key-Words / Index Term :
Indicators Success ,Data Mining, Textual Analysis, Topic Modeling

References :
[1] Saura, Pedro, & Grilo, ”Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining”, Sustainability, 11, 917, 2019.
[2] Saura & Bennett, ” A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis”, Sustainability 2019.
[3] Saura, Reyes-Menendez, & Palos-Sanchez, “Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response”, 2019
[4] Vasile-DanielP?v?loaia, Elena-M?d?linaTeodor, DoinaFotache, & MagdalenaDanilet,” Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences” , 2019
[5] Al-Kabi, Al-Qudah, Dabour, & Izzat Alsmadi,” Arabic / English Sentiment Analysis: An Empirical Study”, 2013)
[6] Kau?mann, et al .”Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining”,2019
[7] Saura, Reyes-Menendez, & Bennett, “How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education”.
[8] Vasile-DanielP?v?loaia, Elena-M?d?linaTeodor, DoinaFotache, & MagdalenaDanilet.,”Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences”, 2019
[9] Mohammed N. Al-Kabi, Alsmadi, Gigieh, & Wahsheh ,“Opinion Mining and Analysis for Arabic, 2014
[10] Mohammed Al-Kabi ,”Arabic / English Sentiment Analysis: An Empirical Study”, 2013
[11] ????? ? ??????,”Web Opinion Mining for Arabic Language", 2016
[12] ,???? ? ?????? "Based Approach for Personalizing Arabic -Semantic Web News" ,2019