ANALYZING TEXT DATA

ANALYZING TEXT DATA

COURSE SYLLABUS

ANALYZING TEXT DATA

I. Course Information:

         Program Title:                     Master of Marketing Studies and Actions  

Course Title:                     Analyzing text data

Course Code:                   

         Number of Credits:                     

           Lecture:                      10,5 hours

II. Lecturer’s Information:

III. Course Description

This course provides students with fundamental knowledge on analyzing text data (text mining). In specific, the course includes the following basic contents: the need for analyzing data in textual form (language), text processing techniques, data analysis methods such as computation. the similarity between documents, text classification (focusing on Naïve Bayes and Decision tree methods are two simple but very effective methods in many problems),…. In addition, the course also presents some applications of text data analysis in marketing such as customer opinion analysis, customer classifications, ...

IV. Course Objectives

Upon completion of this subject, students should be able to:

1. Understand the need and need for text data analysis.

2. Understand and apply the text data analysis processing steps on actual data

3. Proficient application of text data analysis methods

4. Up overall solution to be able to analyze any textual data in reality

5. Proficient in applications such as analyzing customer comments and opinions in online marketing systems.

V. Main Topics

1. Introduction to text data mining

2. Applications of text data analysis

3. Word processing techniques

4. Text analysis methods

a. Calculate the similarity

b. Text classification

5. The problem of analyzing customer opinion

VI. Textbooks and Recommended readings

1. Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.
2. Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., … & Zhou, Z. H. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.

VII. Teaching Methods

No	Activities	Purpose, methods
1	Introduction to text data mining	Lecture note
2	Applications of text data analysis	Lecture note
3	Word processing techniques	Lecture note
4	Text analysis methods	Lecture note
5	The problem of analyzing customer opinion	Lecture note
	Final exam

VII. Assessment Methods

No	Assessment	Grade	Note
1.	Attendance		Students who present less than 70% of the course hours in this course, will not be eligible to take the final exam and must repeat this course.
2.	Group assignment	20%	Group assignment include 3-4 students, presenting results in class and submitting group homework reports
3.	Final exam	80%	Essay questions and exercises
	Total	100%

IX. Other Requirements

Plagiarism is prohibited under any circumstances. Students who commit plagiarism are subjected to failure of the class and possible dismissal from the university.

Follow the procedures during the exams

Follow the policies by International School, Vietnam National University, Hanoiand Nantes University, France