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Computational and Visual Text Analysis (4DV808)

Instructor

Dr. Kostiantyn Kucher

Course Examiner

Prof. Dr. Andreas Kerren

Time & Place

Compare here! The lectures, seminars, and tutoring sessions will take place online via Zoom. Additional video materials will be shared via the Moodle course room.

Teaching Period

III (2021-01-18 till 2021-03-21)

Assessment

Assignments, oral presentations, and oral examinations. Oral examinations will take place via Zoom in week 11.

Prerequisites

90 credits in Computer Science (including a degree project at Bachelor level), and English B/English 6 (or the equivalent).

Credits

5 ECTS

Topic

Introduction to Analytical Methods for Text Data

This course provides an introduction to the variety of analytical methods for text data. Texts surround us in our professional and daily lives in form of written communication, document collections, social media streams, etc. Computer science methods for text analytics can thus be useful for scientific and engineering tasks, including domain applications for literature, social media, or medical text data, for instance.

This course combines two perspectives: computational (i.e., natural language processing) and visual (i.e., information visualization for raw and derived text data) to support various analytical tasks, e.g., topic analysis, opinion mining, and named entity recognition. After finishing the course, the students should be able to choose and develop the most suitable visual text analytic technique for the given task, cf. learning objectives in the course syllabus.

The courses Information Visualization and Advanced Information Visualization and Applications complement this specialized course with a more in-depth view into the theory and state-of-the-art of interactive information visualization and its applications in various domains and for various data types (beyond textual data only). The full-term course Project In Visualization and Data Analysis provides a more applied practical perspective on visual analytics and focuses on project team work on a visual analytic tool implementation.

Schedule

Preliminary Schedule (Lectures):

#	Date	Topic	Slides	Videos
1	2021 01 20	Course Introduction
2	2021 01 27	Foundations of Text Analytics
3	2021 02 03	Text Analytics Tasks and Tools
4	2021 02 11	Foundations of Text Visualization
5	2021 02 17	Lexical and Syntactic Analysis and Visualization
6	2021 02 24	Text Similarity and Semantics
7	2021 03 03	Document Topics and Clusters
8	2021 03 10	Sentiment and Discourse

Preliminary Schedule (Seminars):

#	Date	Topic
1	2021 01 20	Course Assignments Introduction
2	2021 01 27	Assignment 1 Discussion
3	2021 02 11	Assignment 2 Presentation
4	2021 02 24	Work-in-Progress Discussion
5	2021 03 10	Assignment 3 Presentation

Materials

Learning Environment:

Moodle

Visualization Tools and Libraries:

D3 (JavaScript)
Vega (JavaScript; based on D3)
Vega-Lite (JavaScript; based on D3 and Vega)
plotly.js (JavaScript; based on D3)
nivo (JavaScript; based on D3 and React)
Victory (JavaScript; based on D3 and React)
Chart.js (JavaScript)
vis.js (JavaScript)
Highcharts (JavaScript)
Google Charts (JavaScript)
Rickshaw (Temporal data; JavaScript; based on D3)
Leaflet (Geospatial data (maps); JavaScript)
Sigma (Graph/network data; JavaScript)
Bokeh (Python; generates a web-based visualization)
Dash (Python or R; generates a web-based visualization using plotly.js)
plotly.py (Python; generates a web-based visualization using plotly.js)
Altair (Python; generates a web-based visualization using Vega and Vega-Lite)
mpld3 (Python; generates a web-based visualization using D3)
Shiny (R; generates a web-based visualization)
Shiny Dashboard (R; generates a web-based visualization using Shiny)
Tableau (Visualization environment, Dashboards)
QlikView (Visualization environment, Dashboards)
Power BI (Visualization environment, Dashboards)
Visualize Free (Online, Dashboards)
Keshif Online (Online, Dashboards)
yFiles (Several platforms)
Gephi (The Open Graph Viz Platform; Java)
Processing (Environment for graphics programming; several platforms)
Improvise (Java)
JFreeChart (Java)
ggplot2 (R)
Chaco (Python)
Matplotlib (Python)

No longer in active development (not recommended for assignments):

JavaScript InfoVis Toolkit (JavaScript)
Flare - Data Visualization for the Web (ActionScript)
JUNG Java Universal Network/Graph Framework (Java)
InfoVis Toolkit (Java)
JChart (Java)
XmdvTool (Qt)

Interesting URLs:

Assignments

The practical part of the course assessment consists of three assignments forming a single programming project, and the corresponding seminar presentations. Assignment instructions and submission pages will be provided in the Moodle course room.