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  R.E. Loke (Rob)

Assistant Professor Data Science
Telephone:
0621155541
Alternate phone no.:
0621157306
Email:
r.e.loke@hva.nl
Work address:
Wibauthuis, Wibautstraat 3b, 1091 GH Amsterdam
Postal address:
Postbus 526,1000 AM Amsterdam
Room number:
06A42
Expertise:
Online data mining | Pattern recognition | Computer graphics and visualization | Engineering
Social media:
Personal website:
cmihva.nl
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Rob Loke is assistant professor data science at the Centre for Market Insights of the HvA. He holds a MSc degree in computer science as well as a PhD degree, both from Delft University of Technology. He also holds a MA degree in psychology from Leiden University.

Rob was until 2017 stationed and employed in Spain, Portugal, Italy and The Netherlands where he was consultant, researcher, lecturer and ad interim in the public and private sector. He designed, prototyped/built, tested and finetuned a large number of software systems architectures in nationally and European-Union funded projects, individually, but also jointly in teams. Rob authored/coauthored until 2017 about 40 scientific papers in books, journals and conference proceedings. For his PhD-work on the visualisation of sparse datasets from the seabottom he was invited four times to IEEE/OCEANS conferences in the USA—Seattle, Providence, Honolulu and Biloxi. Based on his machine learning system for diatom recognition that he engineered as ad interim data scientist in only three weeks, four new species were identified by taxonomy experts from Royal Botanic Garden Edinburgh.

Rob currently applies his knowledge/expertise to projects in the CMIHvA with regard to online data mining, big data, natural language processing, computer vision and machine learning.

  • Evaluating the impact of B Corp Certification on engagement and satisfaction from the perspective of employees as internal stakeholders
    Myslimaj, R., & Loke-, R. E. (2027). Evaluating the impact of B Corp Certification on engagement and satisfaction from the perspective of employees as internal stakeholders.
  • Identifying Brand Advocates
    Ran, O., & Loke-, R. E. (2027). Identifying Brand Advocates: A Neural Network Approach to Social Media Analysis.
  • Leveraging Innovative Technology for Enhanced Consumer Choice in Fresh Produce
    Lambregtse, T., & Loke-, R. E. (2027). Leveraging Innovative Technology for Enhanced Consumer Choice in Fresh Produce.
  • The Social Scoreboard
    Salesa Pérez, J., & Loke-, R. E. (2027). The Social Scoreboard: Assessing Social Media’s Impact on Athlete and Sponsored Brands.
  • Detecting AI-Generated Reviews for Corporate Reputation Management
    Gc, M., & Loke-, R. E. (2026). Detecting AI-Generated Reviews for Corporate Reputation Management.
  • Exploring the Link between Online Corporate Reputation and Consumer Decision-Making in the Hospitality Industry
    Lehardy, O., & Loke-, R. E. (2026). Exploring the Link between Online Corporate Reputation and Consumer Decision-Making in the Hospitality Industry: A Study of Amsterdam Hotels.
  • The development of a blockchain-based online review system to enhance corporate reputation
    Schoonderbeek, J., & Loke-, R. E. (2026). The development of a blockchain-based online review system to enhance corporate reputation.
  • An Investigation into the Influence of Online Reviews on Corporate Reputation with interactive Knowledge graphs
    Schouten, T., & Loke-, R. E. (2025). An Investigation into the Influence of Online Reviews on Corporate Reputation with interactive Knowledge graphs.
  • Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web
    Sitorus, M., & Loke-, R. E. (2025). Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web.
  • Fake Review Detection of Online Products using Generative Adversarial Learning
    Rustenburg, S., & Loke-, R. E. (2025). Fake Review Detection of Online Products using Generative Adversarial Learning.
  • Quality review classification for the hospitality industry
    Schneider, T., & Loke-, R. E. (2025). Quality review classification for the hospitality industry.
  • Semantic Search on Aspects of Corporate Reputation
    Daalhuizen, T., & Loke-, R. E. (2025). Semantic Search on Aspects of Corporate Reputation.
  • Using soft skills to leverage the innovation dimension of Corporate Reputation
    Kalshoven, T., & Loke-, R. E. (2025). Using soft skills to leverage the innovation dimension of Corporate Reputation: A human capital approach.
  • Corporate reputation through the eyes of social responsibility in the fashion industry
    Loke-, R. E., & Chamlal, K. (Accepted/In press). Corporate reputation through the eyes of social responsibility in the fashion industry. In Marketing and Smart Technologies: Proceedings of ICMarkTech 2023 (Vol. 2). (Smart Innovation, Systems and Technologies; No. 1).
  • Designing business related interaction with reviewers for corporate-reputation insights using an ontology-based chatbot
    Loke-, R. E., & Veizaj, M. (Accepted/In press). Designing business related interaction with reviewers for corporate-reputation insights using an ontology-based chatbot.
  • Presence of Corporate Reputation Cues in Company Vacancy Texts Boosts Vacancy Attractiveness as Perceived by Employees
    Loke-, R. E., & Betten, F. (2024). Presence of Corporate Reputation Cues in Company Vacancy Texts Boosts Vacancy Attractiveness as Perceived by Employees. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA (pp. 593-604) https://doi.org/10.5220/0012863400003756
  • Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine
    Loke-, R., & Pathak, S. (2023). Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine. In Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA (Vol. 1, pp. 559-567) https://doi.org/10.5220/0012136400003541
  • Exploring Corporate Reputation based on Sentiment Polarities That Are Related to Opinions in Dutch Online Reviews
    Loke, R. E., & Vergeer, J. (2023). Exploring Corporate Reputation based on Sentiment Polarities That Are Related to Opinions in Dutch Online Reviews. In A. Cuzzocrea, O. Gusikhin, W. van der Aalst, & S. Hammoudi (Eds.), Proceedings of the 11th International Conference on Data Science, Technology and Applications (Vol. 1, pp. 423-431). Curran Associates, Inc.. https://doi.org/10.5220/0011285500003269
  • Design Options and Learning Analytic Pathways in Doing Agile Scrum Team Work in Education
    Loke, R. (2022). Design Options and Learning Analytic Pathways in Doing Agile Scrum Team Work in Education. In The Paris Conference on Education Official Conference Proceedings: June 16-19, 2022 | La Maison de la Chimie, Paris, France (pp. 1-11). The International Academic Forum (IAFOR). https://doi.org/10.22492/issn.2758-0962.2022.1
  • Assessing corporate reputation from online employee reviews
    Loke-, R. E., & Steentjes, IJ. A. A. (2022). Assessing corporate reputation from online employee reviews. In The 2021 International Conference on Marketing and Technologies: Proceedings by Springer (pp. 243). (Smart Innovation, Systems and Technologies; Vol. 279). https://link.springer.com/chapter/10.1007/978-981-16-9268-0_20
  • Corporate Reputation of Companies on Twitter Seen from a Sustainability Perspective
    Loke, R. E., & Zerouk, I. (2022). Corporate Reputation of Companies on Twitter Seen from a Sustainability Perspective. In J. Luís Reis, M. K. Peter, R. Cayolla, & Z. Bogdanović (Eds.), Marketing and Smart Technologies : Proceedings of ICMarkTech 2021 (Vol. 2, pp. 515-533). (Smart Innovation, Systems and Technologies; Vol. 280). Springer. https://doi.org/10.1007/978-981-16-9272-7_42
  • The Role of Fake Review Detection in Managing Online Corporate Reputation
    Loke, R. E., & Kisoen, Z. (2022). The Role of Fake Review Detection in Managing Online Corporate Reputation. In A. Cuzzocrea, O. Gusikhin, W. van der Aalst, & S. Hammoudi (Eds.), Proceedings of the 11th International Conference on Data Science, Technology and Applications: July 11-13, 2022, in Lisbon, Portugal (Vol. 1, pp. 245-256) https://doi.org/10.5220/0011144600003269
  • Online verkoopstrategieën voor retailers
    Hagen, D., Hopman, M., van Geffen-Wenink, L., Weltevreden, J., van Merriënboer, S., Risselada, A., Loke, R., & Leyh, W. (2021). Online verkoopstrategieën voor retailers: De toegevoegde waarde van onlinestrategieën voor retailers met een fysieke winkel . Platform De Nieuwe Winkelstraat. https://www.platformdenieuwewinkelstraat.nl/dnws-onderzoek/
  • Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web
    Sitorus, M., & Loke, R. (2021). Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web. 34-35. Abstract from 10th International Conference on Data Science, Technology and Applications, Online .
  • Aspect Based Sentiment Analysis on Online Review Data to Predict Corporate Reputation
    Loke-, R. E., & Reitter, W. (2021). Aspect Based Sentiment Analysis on Online Review Data to Predict Corporate Reputation. 343-352. https://doi.org/10.5220/0010607203430352
  • A company’s corporate reputation through the eyes of employees measured with sentiment analysis of online reviews
    Loke, R. E., & Lam-Lion, R. (2021). A company’s corporate reputation through the eyes of employees measured with sentiment analysis of online reviews. 378-385. Paper presented at 10th International Conference on Data Science, Technology and Applications, Online . https://doi.org/10.5220/0010620603780385
  • A new Standard for Doing Agile Scrum Team Work in Education
    Loke, R. (2020). A new Standard for Doing Agile Scrum Team Work in Education. Abstract from 8th European Conference on Education 2020. https://papers.iafor.org/submission57991/
  • Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture
    Loke, R. E., & Kachaniuk, O. (2020). Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture. In S. Hammoudi, C. Quix, & J. Bernardino (Eds.), Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, 310-317, 2020 (pp. 310-317). (DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications). https://doi.org/10.5220/0009892303100317
  • Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms
    Situmeang, F., Loke, R., de Boer, N., & de Boer, D. (2019). Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms. In S. Hammoudi, C. Quix, & J. Bernardino (Eds.), DATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications (Vol. 1, pp. 289-295). (DATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications). SCITEPRESS Digital Library. https://doi.org/10.5220/0007948602890295
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Go to Research database