Social Media Analytics


Social media has changed communication channels and created new challenges for marketing in the digital age. Social media analytics is the methodology of gathering data from vast amounts of semi-structured and unstructured social media data to extract insights that help to make better business decisions.
By the end of the course, the student should:
1. Understand different types of social media and social media analytics
2. Understand social media risks and privacy and ethical considerations
3. Understand social media networks concepts, techniques, and tools
4. Understand text analytics concepts, techniques, and tools
5. Understand sentiment analysis concepts, techniques, and tools
6. Demonstrate capacity to perform a practical work that requires the application of social media analytics techniques
7. Be proficient with text mining, sentiment analysis and social network analysis software

General characterization





Responsible teacher

Vasco Manuel Monteiro


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English




- Text Mining and Analysis: Practical Methods, Examples, and Case Studies using SAS, Goutam Chakraborty, Murali Pagolu and Satish Garla
- Social Media Met rics How to Measure and Optimize Your Marketing Investment, Jim Sterne
- Stand Out Social Marketing: How to Rise Above the Noise, Differentiate Your Brand, and Bu ild an Outstanding Online Presence, Mike Lewis
- Analyzing Social Med ia Networks with NodeXL: Insights from a Connected World, Derek Hansen, Ben Shneiderman, and Marc Smith

Teaching method

The curricular unit is based on mix of theoretical le ctures and practical classes. Each session will introduce new concepts and methodologies, as well as the applications of the learnt concepts using different computational tools. Different learning strategies will be used, such as lectures, slide show demonstrations, step-by-step tutorials on how to approach practical examples, questions, and answers.
The practical component is focused in explor ing the different computational tools by the students, including a discussion on the best approach under different scenarios.

Evaluation method

1. Delivery of individual homework assignments (10%)
2. Exam (50%)
3. Oral presentation of final project (10%)
4. Report of Final Project (30%)
The product can be developed individually or in groups of two students

Subject matter

1. Introduction to social media
2. Social media analytics: an overview
3. Social media networks analytics
4. Social media text analytics
5. Social media sentiment analysis