STA5703 Data Mining Methodology I

BizPro: Extracting and Categorizing Business Intelligence Factors from News

Comparing performance of Naive Bayes, Logistic Regression, and Support Vector Machine for BI classification

By: Dr. Wingyan Chung

Objectives

Upon successfully completing this module, students should be able to:

1. Describe business intelligence and their relevance to data mining.
2. Explain concepts in text categorization and describe prior work on categorizing textual data to assess qualitative information.
3. Relate the components of BizPro to phases of data mining and classification methods.
4. Distinguish among metrics for evaluating performance of text classification algorithms.

Readings

1. News: iPhone 7 and Wireless Headphones: Analyzing Apple's Announcements
2. BizPro: Extracting and categorizing business intelligence factors from textual news articles
3. Text: "Chapter 4. Classification" in An Introduction to Statistical Learning with Applications in R
4. Lecture Slides on BizPro: Extracting and Categorizing Business Intelligence Factors from News
5. "The SVM Classifier"

Text Mining: Research and Applications

1. Word2Vec: Deep Learning of Text Corpus
2. IPM Special Issue: Emotion and Sentiment in Social and Expressive Media
3. Sentiment and network analyses of U.S. Immigration and border security
4. Text Visualization for Authorship Analysis
5. Business stakeholder analyzer: An experiment of classifying stakeholders on the Web
6. Web Searching in a Multilingual World, CACM

Copyright © 2016 Wingyan Chung. All Rights Reserved.