The growth in data generated by social media platforms like Twitter provides a wealth of potentialin formation waiting to be extracted (or mined) – traditionally with a price tag. With the recent advancements in Open Source technologies, specifically Big Data, within the Information Technology world, businesses have started to gather as much information as possible about their customers and market space. The Big Data platform, Hadoop, has become extremely proficient at managing social media data ingestion, storage and processing, due to its ability to use both structured and unstructured data. The aim of this study is to demonstrate a Big Data environment running on Open Source technologies, in order to explore the possibilities of performing geo-located sentiment analytics on Twitter data. Subsequent to this, the link between events and changes in population sentiment was investigated. In this study, an average of 47% of the total tweets ingested were geo-locatable to a country. The Open Source Big Data software was able to demonstrate the reliability of the environment, as well as identify possible limitations to having an environment setup like the one used in this study. A number of research sub-questions were answered, one of which provided information suggesting causality between an event and the change in a populations sentiment when focusing on the events specific topic on Twitter. By performing sentiment analytics on the Twitter data, potential influential users were identifiable for each use case, while allowing additional analytics to be performed and so highlight themes and trends within the data. Three use cases will be concisely addressed in this paper. The first is the Oscar Pistorius Trial (legal), the second is the FIFA World Cup of 2014 (sport), and the last one, a movie titled Maze Runner.

1. Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U., Franklin, M., … & Jagadish, H. V. (2012). Challenges and opportunities with big data: A community white paper developed by leading researchers across the United States. Computing Research Association, Washington, DC, WA.
   
2. Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. Paper presented at 43rd Hawaii International Conference on System Sciences (HICSS), Honolulu, HI. https://doi.org/10.1109/hicss.2010.412
   
3. Chaudhuri, S. (2012). How different is big data? Paper presented at 28th International Conference on Data Engineering (ICDE), Washington, DC, WA. https://doi.org/10.1109/icde.2012.153
   
4. Izhar, T. A. A., Baharuddin, M. F., Mohamad, A. N. & Wan Hasnol, W. M. H. (2016). Using ontology for goal-based query to evaluate social media data. Journal of Advances in Humanities and Social Sciences, 2(2), 108-118.
   
5. Kleiner, B., Stam, A., & Pekari, N. (2015). Big data for the social sciences (FORS Working Paper No. 2015-2). FORS, Lausanne, Switzerland.
   
6. Kumar, S., Morstatter, F., Zafarani, R., & Liu, H. (2013). Whom should I follow?: Identifying relevant users during crises. Paper presented at Proceedings of the 24th ACM Conference on Hypertext and Social Media (pp. 139-147). Paris, France. https://doi.org/10.1145/2481492.2481507
   
7. Laney, D. (2001). 3D management: Controlling data volume, velocity, and variety. Retrieved from https://goo.gl/C5iHn
   
8. McClary, D. (2013). Acquiring big data using Apache flume. Retrieved from https://goo.gl/2qguqN
   
9. Milstein, S., & O’Reilly, T. (2009). The twitter book. Sebastopol, CA: O’Reilly Media.
   
10. O’Leary, D. E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96-99. https://doi.org/10.1109/MIS.2013.39
    
11. Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. Paper presented at International Conference on Collaboration Technologies and Systems (CTS), (pp. 42-47), San Diego, CA. https://doi.org/10.1109/cts.2013.6567202
    
12. Torres, O. H. (2013). Big data evolves. Retrieved from https://goo.gl/CNGDI1
    
13. Weil, K. (2010). Measuring tweets. Retrieved from https://goo.gl/A8RJsM
    
14. Zainuddin, N. A., Norhuda, I., Adeib, I. S., Mustapa, A. N., & Sarijo, S. H. (2015). Artificial neural network modeling ginger rhizome extracted using rapid expansion Super-Critical Solution (RESS) Method. Journal of Advances in Technology and Engineering Research, 1(1), 1-14.

Vyver, A. G. D. V., & Gillies, D. (2017). Analysing public perceptions of international events by using geo-located twitter data. International Journal of Humanities, Arts and Social Sciences, 3(2), 64-70.