DS821: News and Market Sentiment Analytics

It is assumed that students are familiar with the following concepts:

• Basic classification and regression techniques.
• The concepts of train-, validation- and test-split of data.
• Sampling techniques.
• Implementation of mentioned techniques in a programming language.

The courses below, which are all part off the profiles in Data Science all gives a high degree of understanding of the above concepts.

• Economics and Business Administration: DS810: Data-driven Decision Making, DS804: Datamining and machine learning, DS831: Programming for Data Science, DS805: Multivariate Statistical Analysis
  
• Health Data: DS804: Datamining and machine learning, DS831: Programming for Data Science, DS805: Multivariate Statistical Analysis, DS812: Introduction to Basal Biostatistical Terms and Regression
• Environmental Data Science: DS804: Datamining and machine learning, DS831: Programming for Data Science, DS805: Multivariate Statistical
• Human informatics: DS804: Datamining and machine learning, DS831: Programming for Data Science, DS805: Multivariate Statistical Analysis, DS807: Applied machine learning
• ICT Systems: DS820: Discrete methods for Data Science, DS830: Introduction to programming, DS804: Datamining and machine learning, DS807: Applied machine learning, DS805: Multivariate Statistical Analysis

The course provides a foundation for applying the knowledge, competences, and skills, which are acquired in previous machine learning and regressions courses particularly addressing issues related to text analysis to facilitate a basis for news and market sentiment analysis.

In relation to the competence profile of the degree it is the explicit focus of the course to:

• give competences to understand and handle large amounts of text data
• give skills to analyse and reflect on transforming raw text data into useful input for classification and/or regression models
• give knowledge on how to understand and frame questions related to sentiment analysis
• give skills to formulate and identify possible consequences of chosen procedures when handling text data
• give the students a basic toolbox for text analysis in order to establish a basis for future work within NLP and/or sentiment analysis in a broad sense
• give the students knowledge on available NLP and sentiment tools
• give skills to adopt machine learning in combination with text data to achieve useful and presentable results

The learning objective of the course is that the student demonstrates the ability: 

• To write simple programs that manipulate and analyse language data.
• To understand the key concepts of NLP and linguistics used to describe and analyse language.
• To be able to evaluate performance of NLP techniques based on data.

The following main topics are contained in the course: 

• Applications of large language models and transformers
• Leveraging financial news as data
• Basics of NLP
• Deep learning, embeddings and feature extraction
• Text and sentiment classification 
• Text as data    

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

• Intro phase (lectures): 6 hours
• Training phase: 18 hours, including exercises 15 hours.

Activities during the study phase:

• Preperation of lectures
• Preparation of exercises
• Casework (news and/or market analysis)