COURSE UNIT TITLE

: TEXT MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5093 TEXT MINING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Computer Engineering (Non-Thesis-Evening) (English)
Computer Engineering Non-Thesis (English)
Computer Engineering (English)
Computer Engineering (English)
COMPUTER ENGINEERING (ENGLISH)

Course Objective

This course aims to introduce the fundamentals of text mining, presents practical tools to be used in mining process and discusses different case studies based on text mining tools.

Learning Outcomes of the Course Unit

1   Define fundamentals related with Text Mining
2   Learn basic text processing tools and techniques
3   Ability to learn and practice actual machine learning approaches such as Deep Neural Networks in text mining
4   Comprehend tools and techniques to collect, analyze, retrieve, cluster, classify text data
5   Design and implement a Text Mining project for a given problem in an arbitrary domain

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Text Information Systems
2 Background (Basic math and Statistcs)
3 Text Processing with Python
4 Boolean Information Retrieval
5 Weighted Information Retrieval
6 Word Association Mining
7 Word Embeddings
8 Quick Inroduction to Deep Neural Networks
9 Tensor Flow and Keras
10 Text Classification
11 Text Clustering
12 Topic Analysis
13 Opinion Mining and Sentiment Analysis
14 Term project proposal
15 Term project proposal
16 Term project proposal

Recomended or Required Reading

1) ChengXiang Zhai, Sean Massung, Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining, ACM Books, 2016.

2) Jason Brownlee, Deep Learning for Natural Language Processing, 2017.

Planned Learning Activities and Teaching Methods

1) Course will be delivered as lecture in class.
2) Practise topics will be delivered as a demo session on computer to the class
3) Programming homeworks and projects will be assigned to students to better understand and improve the pratical abilities.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc.Prof.Dr.Adil ALPKOÇAK
Dokuz Eylul University, Dept of Computer Engineering
Tinaztepe, 35160 Izmir, Turkey
232-3017408

alpkocak AT^ ceng.deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 15 3 45
Preparations before/after weekly lectures 15 4 60
Preparing assignments 4 10 40
Preparing presentations 1 20 20
Reading 2 15 30
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.153
LO.2553
LO.35553
LO.455535555
LO.555555555555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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