CE807-7-SP-CO:
Text Analytics

The details
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 13 January 2025
Friday 21 March 2025
15
21 March 2024

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

MSC G51512 Big Data and Text Analytics,
MSC G456N3 Computing

Module description

This module will provide an understanding of text analytics and its applications. Students will study state of the art matters for supervised and unsupervised text mining. Methods include rule based traditional machine learning as well as deep neural networks. The module assumes a reasonable programming background and is not suitable for students without prior programming experience.

Module aims

The aims of this module are:



  • To provide students with an understanding of basic and advanced methods of text analytics and its applications.

  • To learn about state of the art methods for unsupervised and supervised text mining including text preprocessing, structured data extraction, clustering of documents, and classification of documents using different techniques. The methods taught include rule-based approaches, traditional machine learning techniques as well as modern Deep Neural Networks.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Have knowledge about methods for text preprocessing.

  2. Understand and use techniques for structured data extraction.

  3. Understand and use various techniques for statistical text analysis.

  4. Apply text analysis methods on data extracted from the web such as social media , websites and others.

  5. Be empowered to independently develop systems for text analytics.

Module information

Outline Syllabus:



  • Text preprocessing techniques.

  • Structured data extraction (such as entities, records).

  • Statistical methods for text clustering (unsupervised learning).

  • Statistical methods for text classification (supervised learning).

  • Deep Learning for text analysis (supervised and unsupervised).

Learning and teaching methods

This module will be delivered via:

  • One 2-hour lecture each week.
  • 2 hours of laboratory time each week.

Bibliography*

This module does not appear to have any essential texts. To see non - essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Lab Exercises & Moodle Quizzes (Weekly)    40% 
Coursework   Assignment: Coding and Output    30% 
Coursework   Assignment: Report & Presentation    30% 

Additional coursework information

The assessment consists of three parts: "Lab Exercises & Moodle Quizzes- Weekly," "Assignment: Coding & Output," and "Assignment: Report & Presentation." In Lab Exercises & Moodle Quizzes- Weekly, during lab time, students will answer some theoretical questions (as multiple-choice, fill in the blanks, etc) and/or do some coding exercises on the code runner. This will be run on Moodle. For the Assignment: Coding & Output, students will develop practical text analytics algorithms to classify (e.g., Sentiment Analysis), generate (e.g., Machine translation), and/or social network combined with text analytics. In Assignment: Report & Presentation, students will summarize and present their lessons. The assessment will be based on the code, performance, efficiency, and report submitted. The Assignment will be handed out in week 37(SU) and submitted to FASer in week 44(SU).

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Ravi Shekhar, email: r.shekhar@essex.ac.uk.
Dr Ravi Shekhar
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770

 

Availability
Yes
No
Yes

External examiner

Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Resources
Available via Moodle
Of 140 hours, 20 (14.3%) hours available to students:
120 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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