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Learning outcomes |
This course aims to equip students with a deep theoretical and practical knowledge of the methodologies, tools, and techniques used to automatically collect, analyze, and interpret data from social networks and the web. It is intended that students understand the importance of social networks in modern society, identify and differentiate types of information (structured and unstructured), and be able to access this information effectively.
The course will also empower students in knowledge discovery, including data analysis, text mining, and graph analysis, using machine learning algorithms and natural language processing models. By the end of the course, students are expected to be proficient in applying these techniques to solve real-world problems, capable of extracting valuable insights and identifying significant patterns in the data.
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Main Bibliography |
[1] Russel, M., (2019). Mining the Social Web, 3rd Edition. O’Reilly.
[2] Vo, L. T. (2019). Mining Social Media: Finding Stories in Internet Data. No Starch Press.
[3] Szabó, G., Polatkan, G., Boykin, P. O., & Chalkiopoulos, A. (2018). Social media data mining and analytics. John Wiley & Sons.
[4] Sarkar, D., Bali, R., & Sharma, T. (2018). Practical machine learning with Python. Apress.
[5] Zafarani, R., Abbasi, M., and Liu, H., (2014). "Social Media Mining". Cambridge University Press.
[6] Steven Bird, S., Klein, E., and Loper, E., (2009). "Natural Language Processing with Python". O’Reilly.
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