IFN647 Text, Web and Media Analytics

Unit Outline: Semester 1 2024, Gardens Point, Internal

Overview

This unit provides an understanding of the principles and techniques underlying the development of Text, Web and social media analysis solutions to some of the varied and complex problems that involve big data. It covers data preprocessing techniques to represent and analyse text, web and social media data. It includes text classification, text clustering and topic modelling methods to understand the text data. It includes web log, structure and content mining to better organise and retrieve data from websites. It teaches you the methods of social network analysis dealing with both the structural and content information embedded within these networks such as sentiment mining, review analysis, etc. This unit is motivated by the ubiquity of unstructured big data in text, Web and social data for which it provides to future professionals and researchers in computer science and data science complimentary approaches to traditional systems

Learning Outcomes

On successful completion of this unit you will be able to:

1. Explain the representation and analysis of textual data and the fundamental approaches for manipulating unstructured data.
2. Apply standard models of Web search and the algorithms to develop new approaches to solve specific problems.
3. Evaluate and justify the results of social media analysis.
4. Implement a major text, web and social media analysis project in a team environment.
5. Communicate professionally in written and visual formats the outcomes of a project to stakeholders.

Content

Learning Approaches

This subject will be delivered through the following means:
Lectures (2 hours) which provide the theoretical basis of the subject;
Practicals (1 hour) which allow you to apply theory to practical (industry data-driven) problems using available software tools.

Feedback on Learning and Assessment

Criteria sheets will be used to provide feedback on assessment of problem solving tasks and projects. Feedback from tutors and lecturers will be provided both directly to students through comments and/or verbally during demonstration of work, and through classroom discussions. Students will be able to meet with the lecturer/s or tutors during designated consultation times to obtain direct feedback and discuss your work and progress in the unit.

Assessment

Overview

This unit provides three assessment items to enable students to understand the principles and techniques introduced in the unit and to show that they have achieved the learning objectives. The Portfolio allows students to explore a range of techniques on selected datasets, demonstrating their mastery of these approaches. The text analysis project allows you to consider a more sophisticated problem, giving you experience with these methods on a more substantial, realworld data set. Finally, the examination will allow you to show your knowledge and understanding of the theory
behind these methods.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Assignment 1

A portfolio of work completed during the semester, including both practical programming exercises and comments or posting of contributions to theoretical topics or questions posed by lecturers.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Assignment 2

A major text analysis project which may be undertaken in a team of 2 or 3.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Exam

A written examination, which covers the material presented in the lectures and workshops throughout the semester.

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.

Resources

Risk Assessment Statement

No unusual risks in the offering of this unit.