Social Media Data Analytics
- Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr.
- Process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data.
- Analyze unstructured data - primarily textual comments - for sentiments expressed in them.
- Use different tools for collecting, analyzing, and exploring social media data for research and development purposes.
- Introduction to Data AnalyticsIn this first unit of the course, several concepts related to social media data and data analytics are introduced. We start by first discussing two kinds of data - structured and unstructured. Then look at how structured data, the primary focus of this course, is analyzed and what one could gain by doing such analysis. Finally, we briefly cover some of the visualizations for exploring and presenting data.Make sure to go through the material for this unit in the sequence it's provided. First, watch the four short videos, then take the practice test, followed by the two quizzes. Finally, read the documents about installation and configuration of Python and R. This is very important - before proceeding to the next units, make sure you have installed necessary tools, and also learned how to install new packages/libraries for them. The course expects students to have programming experience in Python and R.
- Collecting and Extracting Social Media DataIn this unit we will see how to collect data from Twitter and YouTube. The unit will start with an introduction to Python programming. Then we will use a Python script, with a little editing, to extract data from Twitter. A similar exercise will then be done with YouTube. In both the cases, we will also see how to create developer accounts and what information to obtain to use the data collection APIs. Once again, make sure to go item-by-item in the order provided. Before beginning this unit, ensure that you have all the right tools (Python, R, Anaconda) ready and configured. The lessons depend on them and also your ability to install required packages.
- Data Analysis, Visualization, and ExplorationIn this unit, we will focus on analyzing and visualizing the data from various social media services. We will first use the data collected before from YouTube to do various statistics analyses such as correlation and regression. We will then introduce R - a platform for doing statistical analysis. Using R, then we will analyze a much larger dataset obtained from Yelp. Make sure you have covered the material in the previous units before proceeding with this. That means, having all the tools (Anaconda, Python, and R) as well as various packages installed. We will also need new packages this time, so make sure you know how to install them to your Python or R. If needed, please review some basic concepts in statistics - specifically, correlation and regression - before or during working on this unit.
- Case StudiesIn the final unit of this course, we will work on two case studies - both using Twitter and focusing on unstructured data (in this case, text). The first case study will involve doing sentiment analysis with Python. The second case study will take us through basic text mining application using R. We wrap up the unit with a conclusion of what we did in this course and where to go next for further learning and exploration.
- Final oral group examinationThe Exam is planned as an ORAL GROUP EXAMINATION, online on ZOOM Platform. A Student should log in 20 minutes prior to Exam Session. Temporary internet breakdown is for up to 10 min. If longer - a written request to the course director, cc study office manager for further decision to reschedule the Exam for another date for examination: with different exam questions.
- Charu C. Aggarwal. (n.d.). Chapter 1 AN INTRODUCTION TO SOCIAL NETWORK DATA ANALYTICS. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.A1C03FD0
- Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, (2), 897. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.eee.jbrese.v69y2016i2p897.904
- Järvinen, J., & Karjaluoto, H. (2015). The use of Web analytics for digital marketing performance measurement. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.395DE200