Definition, creation and implementation of a social media case study, which is a practical analysis using artificial intelligence and business analytics tools. Data warehouse implementation in SAP HANA of sentiment analysis performed to analyze how users perceive and feel about the selected topic. In addition, use of content generated by ChatGPT for further sentiment analysis.

This course provides an in-depth exploration of sentiment analysis techniques and their applications in business analytics. Participants will learn how to collect and analyze data from a various sources, with a particular focus on Twitter data. The course covers the theoretical foundations of sentiment analysis, practical implementation of sentiment analysis algorithms, and interpretation of results. Participants will apply their knowledge to business scenarios by using ChatGPT generated content for further sentiment analysis.

The main purpose of the case study is to demonstrate how organizations can use sentiment analysis to gain important insights into their customers' feelings and attitudes towards their brand. To this end, the case study first shows how posts on social media can be accessed, then it will show how AI can be used to analyze social media content, and finally how organizations can benefit from these insights.

According to the examination regulation full attendance is intended for a PI.

Students should be able to understand the basic concepts and theories of sentiment analysis in the context of business analysis.

Collect and pre-process data from Twitter for sentiment analysis. Apply various sentiment analysis techniques. Evaluate the performance of sentiment analysis models and interpret the results. Apply sentiment analysis to real-world business scenarios using content generated by ChatGPT. Critically analyze and interpret sentiment analysis results, considering their impact on business decision making.

Assessment will be based on the case studies to be implemented.

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

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