[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118798-en":3,"doc-seo-118798-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118798,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Are we There yet? - Thematic Analysis, NLP, and Machine Learning for Research","Thematic analysis is a widely used qualitative research technique for extracting themes and significant topics from discursive data such as discussions, semi-structured interviews, and comments. Although the process is laborious and time consuming, tools like NVivo, T-Lab, and IRaMuTeQ can streamline analysis and presentation. Recent advances in machine learning and NLP have expanded text analytics, with growing interest in applying automated topic detection, including scenarios using smaller data sets.","Are we There yet? Thematic Analysis, NLP, and Machine Learning for Research  \nElena Fitkov-Norris, Nataliya Kocheva  \nKingston University, Kingston-Upon-Thames, UK  \n[e.fitkov-norris@kingston.ac.uk](e.fitkov-norris@kingston.ac.uk)  \n[n.e.kocheva@kingston.ac.uk](n.e.kocheva@kingston.ac.uk)  \nAbstract. Thematic analysis is a well-established technique for qualitative analysis which is covered in traditional research methods training. The objective of thematic analysis is to elicit themes and significant topics from discursive data such as free style discussions and semi structured or unstructured interviews or comments. The approach is laborious and time consuming and requires a significant input from researchers for identifying and coding the themes although software tools such as NVivo, T-Lab and IRaMuTeQ can aid with results presentation. Recent developments in Machine Learning (ML) and Natural Language Processing (NLP) have boosted interest in text analytics and its applications to social science research. For example, automatic topic identification using ML NLP offers valuable insights in social media analytics. However, machine learning techniques conventionally rely on large data sets to enable the algorithm to elicit themes. More recent research efforts have turned to the performance of machine learning approaches with smaller data sets.  \nThis study aims to compare and contrast the effectiveness of Machine Learning NLP vs human generated themes using the text analytics tools NVivo, T-Lab, IRaMuTeQ, as well as the low-code ML tool KNIME for automatically eliciting themes from academic literature review in the contexts of service operations management research and semi-structured customer interviews. Results indicate that the ML NLP approach has the potential to automatically detect research themes even with small data sets, although the results vary across the different tools and are dependent on the capabilities of the built-in text analytic algorithms. In particular, T-Lab offered the best mapping of machine learning derived topics to researcher themes, and KNIME proved the most robust software, able to derive meaningful topics even with very small sample sizes. The implications for training research students are also significant as they suggest that the inclusion of ML NLP tools and algorithms in the training curriculum of social scientists may be beneficial.  \nKey words: thematic analysis, NLP, machine learning, qualitative data analysis, comparative review  \n1. Introduction  \nQualitative data analysis (QDA) methods have played a significant role in research over the past few decades (Creswell, 2014), and the tools available for such analysis have been expanding rapidly (Meyer and Avery, 2009) . Nowadays, text-analytics software has emerged as a valuable resource that enhances researchers' capabilities and enables more efficient quantification of qualitative data. Among the various techniques employed, Thematic Analysis (TA) stands out as a widely utilized approach, known for its ability to identify, analyse, and report patterns or themes within data (Braun and Clark, 2006) . The objective of thematic analysis is to elicit themesand significant topics from discursive data such as free style discussions and semi-structured or unstructured interviews or comments. This approach allows researchers to delve deeper into the underlying meanings and concepts present in the data, moving beyond surface-level observations and exploring the interconnectedness of ideas. By employing coding and thematic analysis techniques, researchers can uncover rich insights and patterns that contribute to a more comprehensive understanding of the research topic. However, the approach is laborious and time consuming and requires a significant input from researchers for identifying and coding the themes although software tools such as NVivo, T-Lab and IRaMuTeQ can aid with results presentation.  \nRecent developments in Machine Learning (ML) and ","cbCaic0IciVYDTEe","https://ap.wps.com/l/cbCaic0IciVYDTEe","pdf",1375896,1,10,"English","en",105,"# Introduction\n## The role of qualitative data analysis and thematic analysis\n## Machine learning and NLP for text analytics\n## Study aims and research scope","[{\"question\":\"What is the main goal of thematic analysis in qualitative research?\",\"answer\":\"The main goal is to elicit themes and significant topics from discursive data such as discussions, interviews, or written comments.\"},{\"question\":\"How do machine learning and NLP contribute to thematic analysis?\",\"answer\":\"Machine learning and NLP enable automated text analytics, including automatic topic identification, which supports theme detection in research workflows.\"},{\"question\":\"What does the study compare between machine-generated and human-generated themes?\",\"answer\":\"It compares the effectiveness of machine learning NLP approaches against human generated themes using tools such as NVivo, T-Lab, IRaMuTeQ, and KNIME across two datasets.\"}]","Are we There yet? 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