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Research

Our research focuses on the application of computational techniques for understanding and predicting human behavior on digital platforms. Current research projects leverage data science methods, and machine learning to drive domain-specific decisions in a broad selection of business-relevant areas, including, but not limited to, data analytics for social media and electronic commerce, financial data science, and natural language processing for business applications.

Research Focus

Research Focus


Data Analytics for Social Media & Electronic Commerce

We make use of state-of-the-art quantitative methods to understand and predict the dissemination and economic impact of news, comments, and reviews in social media and electronic commerce.



Financial Data Science 

We use data science, natural language processing and machine learning to engineer tools that allow investors, traders, and the financial industry to replace gut decisions with data-driven decision-making processes.



Natural Language Processing for Business Applications 

Our research heavily relies upon the ability to accurately process unstructured text data while distinguishing context and identifying semantics. For this purpose, we actively develop state-of-the-art methods for text mining and sentiment analysis.
Third-Party Projects

Funded Research Projects (ongoing)


Community-Based Fact-Checking on Social Media, Deutsche Forschungsgemeinschaft (DFG), 2022 — 2025.



Rumor Diffusion on Social Media During the COVID-19 Pandemic, Deutsche Forschungsgemeinschaft (DFG), 2021 — 2024.

R Packages

R-Packages


Package: ReinforcementLearning

This package performs model-free reinforcement learning in R. The implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay.


ReinforcementLearning on CRAN

 

Package: SentimentAnalysis

This package performs a sentiment analysis of textual contents in R. The implementation utilizes various existing dictionaries, such as Harvard IV, or finance-specific dictionaries. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.


SentimentAnalysis on CRAN