Semester of Graduation
Electrical and Computer Engineering
First Major Professor
Master of Science (MS)
Data warehouse, OLAP technology and distributed analysis show great potential in improving business analysis, tendency prediction and decision making. With the assistance of data mining techniques, databases can also be a useful tool for analyzing societal trends by gathering data from social media networks. As these networks can contain huge amounts of text data, it can serve as a perfect platform for testing text mining technologies, and discovering what kind of trend or what kind of topic concern people the most during a certain time period. This project utilizes a data set of tweets generated from May to June 2019, which contains more than 2 million tweets with content and location data. After applying some data cleaning techniques, we were able to establish a data cube and provide various analyses based on location. Our results show Twitter users' preference and use frequency varies significantly based on their locations. Ultimately, this project provides a case study about utilizing database, data warehouse and distributed analysis technology to analyze social media, and provides some insight regarding trending topics of interest. This work could be applied by those interested in gaining a better understanding of social media users.
Xu, Wei, "Analyzing social media data and performance comparison with traditional database, data warehouse, and MapReduce approaches" (2020). Creative Components. 568.