| Module Code | CS7IS4 |
| Module Name | Text Analytics |
| ECTS Weighting [1] | 5 ECTS |
| Semester Taught | Semester 2 |
| Module Coordinator/s | Dr. Carl Vogel |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Grasp the scope and limitations of finite state methods in text analysis;
- Apply concepts from model theory within content analytics;
- Analyze, using qualitative and quantitative methods, entailments in natural language texts, distinguishing entailments from suggestions and associations;
- Critically assess text treatments and their appropriateness to analytical methods;
- Comprehend and apply methods of sentiment analysis and metaphor understanding;
- Demonstrate ability to collaborate within a designated team;
- Provide constructive criticism within a scholarly peer review exercise;
- 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:
- Empirically observable properties of natural language and theoretical perspectives on how they arise;
- Formal 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 Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| Academic integrity | Truthful generative AI non-use pledge | LO8 | 1% | 1 | 1 |
| Weekly Research Notebook | Reflections on module content in relation to term project recorded. | L01-L05, L07 | 6% | Starting Week 1 | Weekly |
| Research article summary | Written summaries of associated readings composed individually. | L01-L04, L07 | 6% | Week 1 | Approxi mately Weekly |
| Mid-term Essay | Initial submission of group essay. | L01-L05, L07 | 0.5% | Week 1 | Week 7 |
| Final Archive | Replicability archive of project contributions | LO1-LO5, LO7 | 0.5% | Week 1 | 12 |
| Peer Reviews | Peer reviews of mid-term essays composed. | L06 | 35% | Week 8 | Week 9 |
| Participation | Discussion of readings and lecture material; engagement with allocated groups. | L01-L04 | 1% | Week 1 | Weekly |
| Final Essay | Final group essay submitted archiving team research and in response to peer review. | L01-L05, L07 | 50% | Week 1 | Week 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 |
| Lecture | 22 hours |
| Independent Study (outside scheduled contact hours), broken down by: | 94 hours |
| Preparation for classes and review of material (including preparation for examination, if applicable) | 36 hours |
| Completion of term essays | 47 hours |
| Completion of peer reviews | 11 hours |
| Total Hours | 116 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
N/A
Module Website
Lecture guest link:
To be announced.