Daniel Dan

Dan, Daniel
  • Assistant Professor
Applied Data Science

Short BIO

Daniel Dan holds the position of Assistant Professor in the School of Applied Data Science. He has a university degree in Computer Science and has earned his PhD degree in Managerial and Actuarial Science in the Statistics branch from the University of Udine, in Italy. His fascination with computer science took root, nurtured by intensive study and hands-on experience during his junior and senior years. His student years were marked by a deep engagement with various computer programming projects, laying a solid foundation for his future endeavors.

After his graduation, he remained closely tied to the academic world, enriching it through a series of contracts, collaborations, and teaching roles. His academic journey took him to the University of Padua, Italy, where he spent the first year of his Ph.D. program immersed in the study of Statistics, further broadening his expertise and perspective.

He has a keen interest in text mining methods and sentiment analysis of large text corpora through Large Language Models. His expertise extends to various statistical modeling techniques, integrating machine learning and deep learning algorithms to address complex problems across different fields.

His interests are not confined to these areas alone. He is deeply engaged with the cutting-edge realms of Neural Networks, Transfer Learning, and Reinforcement Learning.

He is a member of the Italian Statistics Society and, aside his research activities, was invited to hold courses at the Vienna University of Business and Economics, The Executive Academy, The School for Gifted Students in Udine, Italy, and the University of Management in Warsaw, Poland.

Research

Participated in the BMVIT project EcoMove (prediction of urban mobility bottlenecks; www.ecomove.at) and the FFG project GENTIO as one of the contributors in the project proposal authors. The topic was predicting communication success of online publications; www.gentio.eu. My contribution to the Happiness Index project was as an author in the project proposal. I contributed to the consulting project Smart Data Analytics for the Hotel Industry, was directly involved in the project Carrying Capacity Methodology for Tourism, as well as Tourism, Case-study on “Inner Areas” post COVID-19. Finally, the EU-funded project DIGITAL-2021-PREPACT: Preparations for the Dataspace for Tourism. The outcomes will change the use and sharing of data as we know them. Currently, I am involved in the EU-funded project OMINO: http://ominoproject.eu.eu

Courses

He teaches (BBA/BSc, MSc/MBA) and supervises theses.

His courses include(ed)

  • Foundations of Artificial Intelligence
  • Mathematics and Statistics 1
  • Applied Linear Algebra
  • Text Mining and Media Analysis
  • Information Systems Management
  • Emerging Tools for New Media and Information Management
  • Basics of Computer Programming
  • Marketing Research and Empirical Project
  • Cases and Technology in Interactive Marketing
  • New Media and e-Business Applications

Projects

Daniel Dan, Lidija Lalicic

Article

Tourist destination / Affinity propagation

Organisations
MODUL University Vienna, Research Center of New Media Technology
Date
2019
Managed By
MODUL University Vienna


Daniel Dan, Thomas Reutterer

Book

Text Analytics for Marketing

Organisations
MODUL University Vienna, Research Center of New Media Technology
Date
2020
Managed By
MODUL University Vienna


Bernd Schuh, Martyna Derszniak-Noirjean, Roland Gaugitsch, Sabine Sedlacek, Christian Weismayer, Bozana Zekan, Ulrich Gunter, Daniel Dan, Lyndon Nixon, Tanja Mihalič, Kir Kuščer,, Miša Novak

Commissioned report

Carrying capacity methodology for tourism

Organisations
MODUL University Vienna, School of Tourism and Service Management, Research Center of New Media Technology, School of Sustainability, Governance, and Methods
Date
11.11.2020
Managed By
MODUL University Vienna


Daniel Dan, Ruggero Bellio, Thomas Reutterer

Conference contribution

A note on latent rating regression for aspect analysis of user-generated content

Organisations
MODUL University Vienna
Date
2018
Managed By
MODUL University Vienna


Daniel Dan, Irem Önder

Article

The Journey of the Maps

Organisations
MODUL University Vienna, School of Tourism and Service Management, Research Center of New Media Technology
Date
2019
Managed By
MODUL University Vienna


Bernd Schuh, Helene Gorny, Manon Badouix, Roland Gaugitsch, Christian Weismayer, Sabine Sedlacek, Tiziana Cei, Daniel Dan

Commissioned report

Case study Italy on “Inner Areas” post COVID-19 // Valchiavenna.

Organisations
MODUL University Vienna, Research Center of New Media Technology, School of Sustainability, Governance, and Methods
Date
2022
Managed By
MODUL University Vienna


Thomas Reutterer, Daniel Dan

Chapter

Cluster Analysis in Marketing Research

Organisations
MODUL University Vienna, Research Center of New Media Technology
Date
2019, 2019
Managed By
MODUL University Vienna

This chapter gives an overview of the various approaches and methods for cluster analysis and links them with the most relevant marketing research contexts. We also provide pointers to the specific packages and functions for performing cluster analysis using the R ecosystem for statistical computing. A substantial part of this chapter is devoted to the illustration of applying different clustering procedures to a reference data set of shopping basket data. We briefly outline the general approach of the considered techniques, provide a walk-through for the corresponding R code required to perform the analyses, and offer some interpretation of the results.


Martin Reisenbichler, Thomas Reutterer, David Schweidel, Daniel Dan

Article

Frontiers: Supporting Content Marketing with Natural Language Generation

Organisations
MODUL University Vienna, Research Center of New Media Technology
Date
25.2.2022
Managed By
MODUL University Vienna

Advances in natural language generation (NLG) have facilitated technologies such as digital voice assistants and chatbots. In this research, we demonstrate how NLG can support content marketing by using it to draft content for the landing page of a website in search engine optimization (SEO).


Martin Reisenbichler, Thomas Reutterer, David Schweidel, Daniel Dan

Paper

Supporting Content Marketing with Natural Language Generation.

Organisations
MODUL University Vienna
Date
2021
Managed By
MODUL University Vienna


Sabine Sedlacek, Christian Weismayer, Daniel Dan

Abstract

Benchmarking tourism destinations along their impact – effect dimensions

Organisations
MODUL University Vienna, Research Center of New Media Technology, Department of Public Governance and Sustainable Development
Date
25.8.2020
Managed By
MODUL University Vienna


Sabine Sedlacek, Christian Weismayer, Daniel Dan, Bernd Schuh

Abstract

The role of institutional stakeholders in developing sustainable tourism regions

Organisations
MODUL University Vienna, Research Center of New Media Technology, School of Sustainability, Governance, and Methods
Date
2022
Managed By
MODUL University Vienna


Alessio Fornasin, Corrado Lagazio, Daniel Dan

Article

Spatial aspects of internal migration in Italy. A longitudinal approach.

Organisations
MODUL University Vienna, Research Center of New Media Technology
Date
8.2019
Managed By
MODUL University Vienna

The paper investigates the internal migration in Italy between 1930 and 2010 in a longitudinal perspective. Risk and distances of migration of first and second order are analysed for people with age 20-49. Data are collected using an ad hoc CATI (Computer-Assisted Telephone Interviewing) system survey, involving around 2,000 respondents born and resident in Italy. A survival model is used for measuring risk of out-migration and OLS (Ordinary least squares) regression models for distance. The variables that emerge as most influencing the risk of migration are the subject’s place of residence and previous life-history. Migratory distance greatly depends on the migrants’ socio-economic background and reason for migrating. The main determinants of internal migration at individual level are education and having previously migrated at a younger age.


Daniel Dan

Article

Exploring the Impact of Negative Words Used in Online Feedback in Hotel Industry: A Sentiment Analysis, N-gram, and Text Network Analysis Approach

Organisations
MODUL University Vienna, Research Center of New Media Technology
Date
22.3.2023
Managed By
MODUL University Vienna

This study examines the words and situations that trigger and those that do not trigger a hotel response when customers post negative online feedback. The research explores through sentiment analysis, bigrams, trigrams, and word networking the valence of online reviews of five important hotels in Las Vegas. Only the feedback that has been categorized as negative by the algorithm is selected. In correspondence to this feedback, the existence of answers from the hotels is checked together with the response style. While the negative valence of the feedback can represent a mixture of subjective and objective emotions, there are common features in their expression. On the responses side from the hotel, not all the reviews receive attention. As such, the negative feedback words are extracted and separated into those that belong to reviews that obtain a response and those that do not. The responses are standard by following an established pattern. This paper aims to contribute to a prominent issue in tourism that is little tackled: responses to feedback. The findings may help the hotel’s management to explore different paths in improving their services and their responses alike. Behavioral marketing researchers might want to use these results to confirm the existence of such patterns in different datasets or in different situations.


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