Natural Language Processing
Natural Language Processing is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course. The total number of contact hours is 15 hours. This leaves 85 hours for private study.
Delivery
This module consists of 2-hour lectures per day for 5 days, plus a 1-hour tutorial per day. During their private study, students should read the course literature, work on practical exercises, and write a project report for the examination. The dates of each lecture are confirmed closer to the start of each term.
Study Hours
Approximately 100 hours of study, of which 15 are contact hours and 85 are private study.
Software Used
Python
Credits
10 CATS credits
Programme Faculty
Key Outcomes
- —Relevant natural language programming techniques and their application to a wide range of empirical issues
- —Integration of natural language programming techniques in econom(etr)ic prediction models
- —Use of the open source statistical software Python for solving NLP problems in economics and business
Desired Skills
- —Engage in abstract thinking by extracting the essential features of large textual corpora to solve economic and managerial problems
- —Communicate and present complex arguments in oral and written form with clarity and succinctness
- —Present, interpret and analyse information in numerical form
- —Utilise effectively statistical and other packages
- —Work effectively both individually and within a team environment
Lecture Programme
- WK 01
Lecture 1: Explorative Textual Data Analysis
- ·Introduction to applications of NLP in economics and management
- ·Text as data
- ·Cleaning textual data
- ·Document feature matrix and word clouds
- WK 02
Lecture 2: Categorizing documents
- ·Unsupervised topic models
- ·Supervised document classification
- WK 03
Lecture 3: Sentiment analysis
- ·Definition of sentiment
- ·Lexicon-based approaches
- ·Improvements
- WK 04
Lecture 4: Time series analysis with sentiment
- ·Sentiment aggregation into time series
- ·(Sparse) prediction and forecasting evaluation
- ·Introduction to the assignment
Assessment
Online coursework submission with the support of your module lecturer and tutor. All sessions are conducted live online at a scheduled time and recorded for review.