Module Database Search
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The latest version of this module is available here
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Text Analytics | |||
| Reference | CMM706 | Version | 2 |
| Created | October 2017 | SCQF Level | SCQF 11 |
| Approved | May 2016 | SCQF Points | 15 |
| Amended | November 2017 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To provide students with a comprehensive understanding of the main principles and practices underlying the retrieval, extraction and mining of text data and the skills to create systems for a variety of information types in differing search environments. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically appraise extraction and search models in information retrieval and Natural Language Processing in relation to big data case studies. |
| 2 | Critically evaluate current research and advanced scholarship in IR and NLP, their role and alternative directions for big data projects. |
| 3 | Combine methods from NLP, topic modelling and text mining tool-kits to develop new extraction processes for real-world tasks. |
| 4 | Plan a comparative study to evaluate and interpret results from designing and developing information retrieval and extraction systems for big data. |
| Indicative Module Content |
|---|
| Comparative analysis of information retrieval and visualisation methods. Text extraction, tokenisation, stemming, bag-of-words, n-gram, statistical language models, vector representations and topic models. Word sense disambiguation, phrase and named entity recognition, POS tagging, shallow parsing, syntax and dependency parsing. Document similarity, clustering and classification, information extraction, sentiment analysis using lexicon-based techniques. Case studies on text classification, topic modelling applied to news articles, intelligent search and browse, sentiment analysis and social media mining. |
| Module Delivery |
|---|
| This is a lecture based course, supplemented with laboratory sessions, where state-of-the-art extraction and retrieval toolkits will be applied to varied case studies. Tutorials will be used to initiate discussions on research papers from the field to supplement the lectures. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 48 |
| Non-Contact Hours | N/A | 102 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | N/A | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Coursework which consists of a written report on the state−of−the−art in a chosen area of information retrieval or text mining research (40%) combined with a class presentation (10%) and a comparative analysis to evaluate methods and systems from NLP, topic modelling and text mining tool kit (50%). | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The student must have a grade D on C1 to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None except for course entry requirements. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | MANNING, C., RAGHAVAN, P., and SCHUTZE, H., 2008. Introduction to Information Retrieval. Cambridge University Press. |
| 2 | RUSELL, A., 2013. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. 2nd Edition. O’Reilly Media. |
| 3 | MANNING, C., and SCHUTZE, H., 1999. Foundations of Statistical Natural Language Processing. MIT Press. |
| 4 | BIRD, S., KLEIN, E., and LOPER, E., 2009. Natural Language Processing with Python. O’Reilly Media. |
| 5 | GABER, M.M., COCEA, M., WIRATUNGA, N. and GOKER, A., 2015. Advances in Social Media Analysis. Springer. |
| 6 | CROFT, W. B., METZLER, D. and STROHMAN, T., 2015. Search Engines Information Retrieval in Practice. Pearson Education Inc. http://ciir.cs.umass.edu/irbook/ |