Computational Text Analysis Methods and an Application for Theory Building
Date: TBD
Time: TBD
Recent developments in Artificial Intelligence shifted our perception of computational applications. These applications present both opportunities and challenges for users, especially researchers, with a notable example being the advent of machine learning-based text analysis methods.
This tutorial seeks to enhance awareness and understanding of text analysis methods while providing a comprehensive overview of applications and platforms for their development. The tutorial is structured into three parts to facilitate a thorough exploration.
The first part will begin with an overview of the current landscape in big textual data applications and potential data sources. Subsequently, scholars will delve into the exemplary software tools employed in these applications. The focus will then shift to an introduction to topic modelling, an unsupervised text analysis application, concluding a showcase of the latest advancements in topic modelling through a practical example.
The second part will involve participants in a hands-on session, employing the round-table work method, a novel approach to theory building in text analysis. This approach analyses existing publications and builds new knowledge with the participation of participants. This interactive session aims to provide participants with practical experience and a deeper understanding of the concepts presented.
The final part will involve a closing discussion on the applications and results of the round-table work, offering participants an opportunity to reflect on the process, reconstructed knowledge, and the practice of theory building in text analysis.
By participating in this tutorial, attendees will gain a comprehensive understanding of text analysis methods, insights into alternative data sources, and knowledge of the practical application of an innovative approach to theory building in this evolving field.
Outline
- Presentation
- Brief introduction to Big Textual Data and Data Sources
- Alternative software tools (Python, R Studio, KNIME Analytics, WEKA)
- Pros and Cons
- Recent Applications of Text Analysis general application purposes
- Introduction to Topic Modeling
- Python Topic Modeling example
- User Perspective of Gensim Library
- Theory Building Examples from Literature and Steps
- Example application
- Introduction to the example topic
- Group Work
- Information about the Process
- Defining the expectation
- Round Table work
- Information about the Process
- Discussion on the Group Work Results
Target Audience
This tutorial is open for any conference participant who wants to get knowledgeable about the recent developments in text analysis applications. Although there are not any prerequisites or required knowledge, we suggest participants to have basic computer (MSOffice and Google Docs) and smartphone (QR Code, Online Survey Filling) usage knowledge. Participants should bring their smartphones with them.
Presenters Information
Eyyub C. Odacioglu is a Management of Projects PhD candidate at Department of Engineering Management in the University of Manchester. His research is about the management of Complex Innovation Projects. Part of his research project, he has developed his own methodology that combined a topic modelling algorithm with the constructivist grounded theory methodology. Meantime he is working as a project manager at an aerospace company in Turkey. He has more than 10 years of professional experience.
Dr Azar Shahgholian is a Senior Lecturer in Digital Marketing at Liverpool Business School, Liverpool, UK. She holds a PhD in Business and Management from The University of Manchester. Her research interests lay in applying Business Analytics to organisations, with a focus on digital marketing, business analytics maturity and coping with digital transformations, She is using Machine Learning algorithms and Big Data Analytics, including text mining and topic modelling in her research and teaching activities, included on externally funded research projects in collaboration with industry. She has published in and reviewed for a number of IS and OM journals and conferences.