دانلود مقاله ISI انگلیسی شماره 43919
عنوان فارسی مقاله

BizPro: استخراج و طبقه بندی عوامل هوش کسب و کار از اخبار مقالات متنی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
43919 2014 13 صفحه PDF سفارش دهید 8950 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
BizPro: Extracting and categorizing business intelligence factors from textual news articles
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Information Management, Volume 34, Issue 2, April 2014, Pages 272–284

کلمات کلیدی
هوش کسب و کار - استخراج عوامل BI - دسته بندی - پروفایل - اخبار آنلاین
پیش نمایش مقاله
پیش نمایش مقاله BizPro: استخراج و طبقه بندی عوامل هوش کسب و کار از اخبار مقالات متنی

چکیده انگلیسی

Company movements and market changes often are headlines of the news, providing managers with important business intelligence (BI). While existing corporate analyses are often based on numerical financial figures, relatively little work has been done to reveal from textual news articles factors that represent BI. In this research, we developed BizPro, an intelligent system for extracting and categorizing BI factors from news articles. BizPro consists of novel text mining procedures and BI factor modeling and categorization. Expert guidance and human knowledge (with high inter-rater reliability) were used to inform system development and profiling of BI factors. We conducted a case study of using the system to profile BI factors of four major IT companies based on 6859 sentences extracted from 231 news articles published in major news sources. The results show that the chosen techniques used in BizPro – Naïve Bayes (NB) and Logistic Regression (LR) – significantly outperformed a benchmark technique. NB was found to outperform LR in terms of precision, recall, F-measure, and area under ROC curve. This research contributes to developing a new system for profiling company BI factors from news articles, to providing new empirical findings to enhance understanding in BI factor extraction and categorization, and to addressing an important yet under-explored concern of BI analysis.

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