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Alexander HogenboomIn this era of ever-increasing computational power, one of the key questions is how we can use such technological progress to our advantage. How can we turn sheer computational power into more intelligent systems? How can we utilize such systems in today's business and economic processes for, e.g., tracking, monitoring, or supporting complex decision making processes? These are questions that inspire my research, yet - paradoxically - I am motivated even more by my doubts as to whether we will ever be able to find definitive answers to questions like these at all. It is the question that drives us.

My name is Alexander Hogenboom and I hold both a Bachelor of Science degree and a cum laude Master of Science degree in Economics and Informatics, with a specialization in Computational Economics. Moreover, I have a PhD degree in Business Intelligence. I am currently affiliated with Erasmus University Rotterdam in the Netherlands as a researcher through a hospitality agreement, after having obtained my PhD degree at this university. I perform my research within the Econometric Institute. Additionally, in the context of my PhD research, I have been affiliated to the research center for Business Intelligence at the Erasmus Research Institute of Management, as well as to Erasmus Studio. Please take a look around on this page in order to find out more about my research endeavors, my publications, as well as my related activities. Welcome to my personal web page!


In general, my research interests all relate to the utilization of methods and techniques from informatics and computer science for facilitating or supporting decision making processes. This has evolved from research related to semantic information systems (mash-ups, query optimization), while obtaining my Bachelor of Science degree, to research in the field of decision support systems (dynamic pricing), while obtaining my Master of Science degree. In the context of my PhD research, my main interests have shifted more towards intelligent systems for information extraction, or more specifically for tracking and monitoring of (economic) sentiment.

PhD Project

The recent turmoil in the financial markets has once again demonstrated how crucial it is for decision makers to identify issues and patterns that matter and to track and predict emerging events. A key element for decision makers to track here is their stakeholders' sentiment. Investor sentiment influences financial markets. Consumer sentiment influences how people spend their money. In general, decision makers have to understand what is going on in their domains and, more specifically, what is driving their stakeholders. What do people think about the economy? About products? Brands? Companies? And where does this sentiment come from?

Nowadays, the Web allows users to produce an ever-growing amount of virtual utterances of opinions in reviews, blogs, tweets, and so on. This yields a massive amount of data, containing traces of valuable information - people's sentiment with respect to products, brands, etcetera. This information can be extracted from textual data by means of sentiment analysis techniques. Typically, the goal of such techniques is to (semi-)automatically determine the polarity of natural language texts.

An intuitive approach here would involve scanning a text for cues signaling its polarity, e.g., positive or negative words. However, when doing so, we may be ignoring important information: the information conveyed by structural aspects of a piece of natural language text. For instance, a conclusion may play a different role in conveying the overall sentiment of a text than a piece of contrasting information does.

Therefore, the goal of my PhD research project was to advance the state-of-the-art of sentiment mining by developing and utilizing models, methods, and algorithms for harvesting the information conveyed by structural aspects of natural language text. This research was linked to the Argumentation Discovery in Economics Literature project of the Erasmus Research Institute of Management. This work has been carried out in the context of the Semantic Scholarly Publishing project of Erasmus Studio as well. Last, I have also performed my research in the context of the COMMIT Infiniti project on Information Retrieval for Information Services, work package three. My promotors were Uzay Kaymak and Franciska de Jong, whereas Flavius Frasincar was my daily supervisor.

I have successfully defended my dissertation on and the accompanying on Friday November 13, 2015. My dissertation can also be found in the repository of Erasmus University Rotterdam.


Developed software and data used in my ongoing research will be made available on this page.


As my research interests in the area of Business Intelligence vary, my work has led to peer-reviewed publications in various fields. These fields include Information Extraction, Decision Support Systems, and Semantic Information Systems. Some of these publications have been indexed by DBLP and Google Scholar as well.

Information Extraction
Decision Support Systems
Semantic Information Systems


The work I have performed has involved some activities other than just performing research as well. Over the years, I have been involved with several courses and I have performed several (co-)supervision activities.

  • RDF Chain Query Optimization in a Distributed Environment
    E. Niewenhuijse (Bachelor's Thesis, February 2013).
  • Exploiting Rhetorical Structure of Text in Sentiment Analysis for Decision Support
    B. Heerschop (Master's Thesis, September 2012).
  • A Multi-Feature Approach to Sentiment Summarization of Online Conversational Documents
    G. Mangnoesing (Bachelor's Thesis, August 2012).
  • A Linguistic Approach for Searching Economic News
    K. Schouten (Master's Thesis, May 2012).
  • Cross-Language Sentiment Normalization through Interchangeability between Sentiment Analysis and Universal Star Rating
    M. Bal (Bachelor's Thesis, July 2011).
  • Adding Emoticon Semantics to Sentiment Analysis
    D. Bal (Bachelor's Thesis, July 2011).
  • Using IT to Improve Knowledge about Political-Economic Space
    M. Jongmans (Master's Thesis, August 2010).
  • Supporting Multiple Languages in Sentiment Analysis
    B. Heerschop (Bachelor's Thesis, August 2010).
  • Negation Improvements in Sentiment Analysis
    P. van Iterson (Bachelor's Thesis, August 2010).
  • Review Sentiment Categorization Using a 5-Star Scale
    F. Boon (Bachelor's Thesis, July 2010).


Besides my research, teaching, and supervision activities, I have spent some of my time on some other work-related activities as well. Some of my efforts have also resulted in honors and awards.

Honors and Awards
  • IEEE SMC student travel grant for attending and presenting at the 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2011), Anchorage, Alaska, United States, October 9-12 2011.
  • IEEE SMC student travel grant for attending and presenting at the 2010 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2010), Istanbul, Turkey, October 10-13 2010.
  • Honorable Mention Award for paper at the Eleventh International Conference on Electronic Commerce (ICEC 2009), Taipei, Taiwan, August 12-15 2009.
  • TAC 2009 student travel grant for attending and presenting at the Workshop on Trading Agent Design and Analysis (TADA 2009) at the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, California, United States, July 11-17 2009.


Alexander Hogenboom, PhD.

Econometric Institute
Erasmus School of Economics
Erasmus University Rotterdam

Burgemeester Oudlaan 50
3062 PA Rotterdam
The Netherlands
P.O. Box 1738
3000 DR Rotterdam
The Netherlands
Room: H10-21
Phone: +31 (0)10 408 1262
Fax: +31 (0)10 408 9162
Profiles: LinkedIn
Google Scholar