|Termin||Fr 13. Mai 2022|
|Kurzfassung||In the last decade, ample research has been produced regarding the value of user-generated data from microblogs as a basis for time series analysis in various fields.In this context, the objective of this thesis is to develop a domain-agnostic framework for mining microblog data (i.e., Twitter). Taking the subject related postings of a time series (e.g., inflation) as its input, the framework will generate temporal data sets that can serve as basis for time series analysis of the given target time series (e.g., inflation rate).
To accomplish this, we will analyze and summarize the prevalent research related to microblog data-based forecasting and analysis, with a focus on the data processing and mining approach. Based on the findings, one or several candidate frameworks are developed and evaluated by testing the correlation of their generated data sets against the target time series they are generated for.
While summative research on microblog data-based correlation analysis exists, it is mainly focused on summarizing the state of the field. This thesis adds to the body of research by applying summarized findings and generating experimental evidence regarding the generalizability of microblog data mining approaches and their effectiveness.