CS7IS4 – Text Analytics

Module CodeCS7IS4
Module NameText Analytics
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 2
Module Coordinator/s  Dr. Carl Vogel

Module Learning Outcomes

On successful completion of this module, students should be able to:

  1. Grasp the scope and limitations of finite state methods in text analysis;
  2. Apply concepts from model theory within content analytics;
  3. Analyze, using qualitative and quantitative methods, entailments in natural language texts, distinguishing entailments from suggestions and associations;
  4. Comprehend and apply methods of sentiment analysis and metaphor understanding;
  5. Demonstrate ability to collaborate within a designated team;
  6. Provide constructive criticism within a scholarly peer review exercise;
  7. Collaboratively compose a scholarly research article informed by the literature, novel exercises in text analytics and responding to peer review.

Module Content

Specific topics addressed in this module include:

  • Finite state methods for representation and reasoning, model theoretic semantics;
  • Meaning preserving syntactic alternations, text-entailment, text associations;
  • Formal language theory;
  • Statistical Language Processing;
  • Sentiment and metaphor analysis.

Teaching and Learning Methods

Lectures, readings, discussion of readings, laboratory notebook record keeping, team meetings, team collaboration, peer review.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
Weekly Research
Reflections on module
content in relation to term
project recorded.
L01-L05, L0710%Starting Week 1Weekly
Research Article
Written summaries of associated readings composed individually.L01-L04, L0710%Week 1Approxi mately
Mid-term EssayInitial submission of group essay.L01-L05, L071%Week 1Week 7
Peer ReviewsPeer reviews of mid-term essays composed.L0626.5%Week 8Week 9
ParticipationDiscussion of readings and lecture material.L01-L042.5%Week 1Weekly
Final EssayFinal group essay submitted archiving team research and in response to peer review.L01-L05, L0750%Week 1Week 12

Reassessment Details

Essay (100%).

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:22 hours
Lecture22 hours
Independent Study (outside scheduled contact hours), broken down by:98 hours
Preparation for classes and review of material (including preparation for examination, if applicable)36 hours
Completion of term essays 51 hours
Completion of peer reviews 11 hours
Total Hours120 hours

Recommended Reading List

  • Ido Dagan, Dan Roth, Mark Sammons, Fabio Massimo Zanzotto (2013) Recognizing Textual Entailment: Models and Applications. Morgan Claypool.
  • Carol Genetti (2019) How Languages Work: An Introduction to Language and Linguistics. Cambridge University Press. 2nd Edition.
  • Dan Jurafsky and James H. Martin (2009) Speech and Language Processing (2nd ed.) Pearson.
  • Beth Levin (1993) English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press.
  • Bing Liu (2014). Sentiment Analysis and Opinion Mining. Cambridge: Cambridge University Press.
  • CD Manning and H. Schutze (1999) Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.

Module Pre-requisites

Prerequisite modules: N/A

Other/alternative non-module prerequisites: N/A

Module Co-requisites


Module Website


Lecture guest link:

Monday, February 8, 14:00-15:50: https://eu.bbcollab.com/guest/7fd1b6a46a2a459dbb5ec5e0cb7ed962