ETM5800 · Text analytics for business

Overview

Today, many organisations face massive amounts of unstructured textual data such as social media data, product, and service reviews, the information generated from company websites, and others. However, the phrase “Data is the new oil” is only valid if companies can harness valuable insights from extensive unstructured data and use this to guide them in making better decisions for the company. This course will provide the analytical tools to extract information from unstructured business-related textual data, derive patterns and trends, cluster the data, make inferences, and finally communicate or make predictions about the data. The course introduces powerful text analytical techniques using relevant computer software to administer these techniques. The lessons will begin with motivations for exploring text, identifying text format types, and other principles governing text data. After that, there will be an introduction to various text analysis software and the use of software to extract, clean, and inspect documents. The analysis section will begin with descriptive statistics and visualisation of textual data, followed by opinion mining using sentiment analysis, analysing word frequency and documents using tf-idf and examining relationships between words using n-grams and correlations. The course will then demonstrate the use of unsupervised machine learning topics to categorise information and discover hidden semantic structures in text data. Examples of these techniques are such as cluster analysis, topic modeling, word embeddings, and document embeddings. You will also be exposed to document classification models and techniques to fit and evaluate them. Finally, the course will discuss text data application for prediction and social network analysis. All practice exercises for the different text analytical methods will infuse real-world business examples to equip you with tools to relate text analytics with the practical business scenario.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. develop text analytical skills ranging from text extraction, pre-processing of text, descriptive statistics and visualisation of text, clustering text, sentiment analysis, word and document embeddings and social media analysis
2. apply text analytical techniques to current business case studies by utilising topical datasets
3. derive critical insights and predictions from textual data and communicate these results effectively.
4. develop critical programming skills to work with textual data using prominent software.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.