تحلیل اطلاعات رسانه های اجتماعی و هوش تجاری
ترجمه نشده

تحلیل اطلاعات رسانه های اجتماعی و هوش تجاری

عنوان فارسی مقاله: مدل هوش تجاری برای تحلیل اطلاعات رسانه های اجتماعی
عنوان انگلیسی مقاله: Business Intelligence Model to Analyze Social Media Information
مجله/کنفرانس: پروسیدیای علوم کامپیوتر - Procedia Computer Science
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: مدیریت فناوری اطلاعات، مدیریت کسب و کار، مدیریت عملکرد، مدیریت منابع اطلاعاتی
کلمات کلیدی فارسی: رسانه های اجتماعی، نایو بیز، ماشین بردار پشتیبانی، درخت تصمیم گیری، هوش تجاری
کلمات کلیدی انگلیسی: Social Media، Naive Bayes، SVM، Decision Tree، Business Intelligence
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.procs.2018.08.144
دانشگاه: Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
شناسه ISSN: 1877-0509
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E12835
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related Work


3- Methodology


4- Proposed Model


5- Results and Discussion


6- Conclusion and Future Work


References

نمونه متن انگلیسی مقاله

Abstract


Social media is a platform to share information that is very liked by everyone nowadays because some of the facilities that make it easier for us to communicate with each other, share documents, chat and even create a community. In addition, we can also analyze the content of social media by using several methods in data mining, so that we can get new the information to support decision making that can bring benefits to individuals and companies. The purpose of this research, to create a business intelligence dashboard to observe the performance of each Topic or channel of news posted to social media accounts such as Facebook and Twitter. Topical performance in social media is the number of Topics in articles posted to social media getting like, share, comment etc. To be able to know the Topic of a news post in social media, used some text classification techniques such as Naive Bayes, SVM and Decision Tree. The comparative results of the algorithms are taken which has the best accuracy of SVM for subsequent implementation in the data warehouse. Meanwhile, the business intelligence dashboard data source will be sourced from the data warehouses that have been made before.


Conclusion and Future Work


After evaluating the performance of the classification algorithm, the best performing algorithm is SVM with 78.99% accuracy, then the second rank is Naive Bayes has an accuracy of 74.67% and the last one is Decision Tree has an accuracy of 57.66%. Data Warehouse System created as a data source for Business Intelligence System can run the data calculations and summarization automatically, so no longer need to calculate manually. In addition, the business intelligence system created can be accessed by user anytime and anywhere. The data warehouse system was created as a data source application for Business Intelligence that can automatically perform calculations and data summaries, so it is no longer necessary to calculate manually the number of comments, the number of Likes and the number of shares per article to be matched with the topics of article. The Business Intelligence application is very useful for monitoring the performance of news posted on social media both from the internal company account and from competitors in real time, so it is no longer necessary to visit one by one account in social media. This study will be continued by developing Business Intelligence applications that can be implemented on other social media platforms such as Instagram, Linkedin, Path and others. In addition, the implementation of data warehouses and OLTP using Big Data technology allows faster processing of distributed data so that the data can be viewed on the dashboard in real time.

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