Course overview
This course provides students an overview of contemporary natural language processing with students gaining hands-on experience implementing NLP with real-world data. This course aims to equip learners with the knowledge and skills to analyse textual datasets. Building upon concepts of data science practice, this course will look at the challenges and methods used for textual datasets. By examining the methods of tokenization, sentiment analysis and topic modelling, the course shows how patterns in textual data can be identified.
- Text and Social Media Data
- Analysing and Modelling Language
- Text Analytics in Practice
Course learning outcomes
- Experiment with text reading and manipulation techniques in R (tokenisation, stop words and word embedding) to prepare text data.
- Explain the importance of representative datasets and the ethics of using text from social sources.
- Apply sentiment analysis and regression to real-world datasets to aid prediction and uncover patterns and themes.
- Interpret and evaluate the outputs of sentiment analysis and topic modelling to generate valid and novel insights.
Degree list
The following degrees include this course