H8TA - Text Analytics

Module Code: H8TA
Long Title Text Analytics
Title Text Analytics
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator:  
Module Author: Isabel O'Connor
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Master’s / PhD degree in a computing or cognate discipline. May have industry experience also.

 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Rationalise and defend methodological choices in pre-processing methods for text analytics
LO2 Build and critically evaluate text analytics models in a variety of contexts
LO3 Execute and document corpus-based case studies
LO4 Evaluate and discuss the impact machine learning models applied to text corpora
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
 

Module Content & Assessment

Indicative Content
Introduction
Introduction to:. Text Analytics, . Key Domain, Methods and Ethics. and software libraries / packages, and Web APIs. E.g.: NLTK, LIWC, S4, GATE, Alchemi API, Natural Language API, Mallet, tm/tidyverse/tidytext etc.
Vector and Document Spaces
Elementary Methods:. Bag(s) of Words, . Ngrams . Document and Language Classification via Vector spaces and the Zipfian Distribution. Dictionary-based approaches
Vector and Document Spaces
Vector Spaces: Term Document / Document Term Matrices, TF-IDF, Word2Vec, Doc2Vec
Text Understanding and Semantics
Topic Modelling:. Latent Dirichlet Allocation. Explicit Semantic Analysis. Latent Semantic Analysis. Hierarchical Dirichlet Process. And associated methods, e.g.. Singular Value Decomposition. Non-negative Matrix Factorisation
Text Understanding and Semantics
Part of Speech Tagging Entity Extraction / Identification, SPARQL and Linked Data, Aspect-based Reasoning
Knowledge Graphs and Network Analysis
Introduction to graph-based models for document corpora, Introduction to network analysis for graph-based models
Computational Linguistics
Interrogating structure, intent, language use independent of content; key use cases:. Affect Analysis. Deception Detection. Psychometric Profiling. Author fingerprinting
Applied Machine Learning
Case Studies in applying (un)supervised machine and/or deep learning to text analytics.
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Ongoing independent and group problem solving activities and feedback.
Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
Students will submit a report (4000 words) on a case study where they will encompass 3 methods covered in the first 6 teaching weeks as outlined in the indicative structure above. The report should discuss the preparation of the corpora for each method, and rationalise the use and effectiveness of each method applied. It should also discuss related work in the area covering the context of the text data as well as studies applied to similar data sets
Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
Students will submit a report (4000 words on a case study where they will encompass a further 2 methods from the teaching weeks 7-10 and a further 2 methods not yet included applied in conjunction with a selection of machine learning models: at least 1 unsupervised, and at least 1 supervised. The report should discuss the preparation of the corpora for each method, and rationalise the use and effectiveness of each method applied. It should also discuss related work in the area covering the context of the text data as well as studies applied to similar data sets.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Should learners not achieve a 40% pass mark, they will either sit a repeat terminal exam, or undertake an assessment that assesses all learning outcomes.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 24 Per Semester 2.00
Independent Learning Independent learning (hours) 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Bird, S., Klien, E. & Loper, E.. (2009), Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O’Reily.
  • Goldberg, Y.. (2017), Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies), Morgan & Claypool Publishers.
  • Silge, J.. (2017), Text Mining with R: A Tidy Approach, O’Reily.
  • Rodrigues, M., & Teixeira, A. (2015), Advanced Applications of Natural Language Processing for Performing Information Extraction, Springer.
Supplementary Book Resources
  • Biemann, C. & Mehler, A.. (2014), Text Mining, Springer.
  • Pennebaker, J.. (2013), The Secret Life of Pronouns: What Our Words Say About Us, Bloomsbury Press.
  • Sankar, D.. (2016), Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data, Apress.
  • Wachsmuth, H.. (2015), Text Analysis Pipelines, Springer.
This module does not have any article/paper resources
Other Resources
Discussion Note: