CS803 - Information Analysis
| TIMETABLE | TEACHING MATERIAL |
| Credits | 10 |
| Level | 5 |
| Semester | Semester 1 |
| Availability | Mandatory |
| Prerequisites | N/A |
| Learning Activities Breakdown | Lectures: 10 | Tutorials: 10 | Labs: 0 | Assignment 50 | Self Study: 30 |
| Items of Assessment | 2 |
| Assessment | Individual assignment (30%), Group assignment (70%) |
| Lecturer | Ian Ruthven |
Aims and Objectives
This course will introduce students to the concept of information analysis, covering major techniques in information analysis including sentiment analysis, content analysis, information visualisation, systematic reviews and summarisation.
Learning Outcomes
On completing of the class students will be able to:
- Demonstrate an understanding of information analysis as an area of information work.;
- Appreciate the range of manual information analysis techniques;
- Appreciate the range of automatic information analysis techniques;
- Develop an understanding of how to solve an information analysis problem;
Syllabus
- Introduction: information analysis and data science;
- Getting data and understanding it;
- Content analysis;
- Systematic reviews;
- Clustering;
- Automated classification;
- Sentiment analysis;
- Visualization and presentation.
Recommended Reading
This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.
‘A review of clustering techniques and developments’. Saxena, Amit, Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W. and Lin, C. T. Neurocomputing 267 (2017): 664-681.
‘Sentiment analysis: A review and comparative analysis of web services’. Jesus, S. G., Olivas, J. A., Romero, F. P., and Herrera-Viedma, E., Information Sciences 311 (2015): 18-38.
The content analysis guidebook. Neuendorf, K. A., Sage 2016.
Last updated: 2024-08-07 16:35:58