Text and Image Mining for Business Research

The first edition of this course took place in April 2023 and received an average course grade of 4.70 (1= lowest grade – 5= highest grade) with a response rate of 85% (11 participants).

Time and place: June 17-20, 2024. 

Learning goal and objectives:

The objective of this course is to contribute to students’ knowledge on how to use text mining methods, processes, and tools for business research (focus in marketing). In addition to text, the last part of the course will involve work with image mining. The learning goal is to prepare students for the conceptual and empirical challenges involved in the use of unstructured data (text and images) for marketing models. The course will cover topics such as theories of language, measurement error, validation, machine learning, and endogeneity issues.

Instruction and examination:

The course is built on prior reading of recent guides and empirical applications of text and image mining, and hands-on work on class examples based on recent datasets from sources such as Twitter, Instagram, TripAdvisor, and Yelp. The course will cover four main units: 1) Introduction to Methods and Theories for Unstructured Data, 2) Dictionary Methods, 3) Machine/deep Learning (supervised and unsupervised), and 4) Multimodality and Image Measurement. 

The course involves theory sessions, hands-on examples (carried out in parallel with the student), individual applications and post discussion/consultations. Before the start of the course there will be a remote on-boarding session and individual work related to the course’s main software (KNIME Analytics). The course will be helpful for doctoral students’ decision-making concerning data gathering, measurement, modelling, and manuscript positioning.

Credits: 4 ECTS

Grading: The course will be graded as Pass or Fail, based on the completion of the applications carried out before and throughout the course.

Prerequisites: Programing and basic statistical knowledge is recommended. The course will use KNIME Analytics as the main analytics software, together with limited integrations of R and Python. Students attending the course will need to install all the required software and extensions before attending. It is compulsory to have a laptop with at least 8GB RAM (16 GB RAM would be recommended).


  • Max. 25 students are admitted to the course.
  • Application guidelines:  PhD. students in all fields of economics and business studies, (e.g., in marketing, management, accounting, etc.). As most of the literature and examples relate to marketing and management, priority will be given to doctoral students who are majoring in those disciplines. Participants can apply by sending their CV and a motivation letter to Robert Ciuchita ( robert.ciuchita@hanken.fi).   
  • Last application date: 15.2.2024
  • Notification of acceptance: 15.03.2024.


Francisco Villarroel Ordenes, Assistant Professor of Marketing, Luiss Guido Carli (https://impresaemanagement.luiss.it/docenti/cv/354198). 

Dr. Francisco Villarroel Ordenes is an Assistant Professor and Director of the MSc. in Marketing at LUISS Guido Carli University where he teaches Business & Marketing Analytics, Performance Marketing, and Unstructured Data Analysis. His research revolves around the themes of marketing analytics, social media marketing, and customer experience, and it has been published in leading journals including the Journal of Consumer Research, Journal of MarketingJournal of Service ResearchJournal of Retailing, among others. He currently serves on the editorial review boards of the Journal of Consumer ResearchJournal of RetailingJournal of Service Research, and Journal of Business Research.  

Course coordinator and contact information:

Robert Ciuchita, Assistant Professor, Department of Marketing, Hanken School of Economics (https://harisportal.hanken.fi/en/persons/robert-ciuchita)

Dr. Robert Ciuchita is an Assistant Professor of Marketing and director of the Bachelor in Business programme at Hanken School of Economics (Finland). His research and teaching interests lie in the areas of service management, service innovation, digital marketing, and customer experience management. His research has appeared in, for example, the Journal of Service ResearchJournal of Interactive MarketingJournal of Business Research, Journal of Service Management and Journal of the Association for Consumer Research. He currently serves as an associate editor (Innovation & Technology) for the Journal of Business Research and on the editorial review boards of the Journal of Service Research and Industrial Marketing Management.