This book establishes constructivist, interpretivist, and linguistic approaches based on conventions about the nature of qualitative and text data, the author’s influence on text interpretation, and the validity checks used to justify text interpretations. Vast quantities of text and qualitative data in organizations often go unexplored. Text analytics outlined in this book allow readers to understand the process of converting unstructured text data into meaningful data for analysis in order to measure employee opinions, feedback, and reviews through sentiment analysis to support fact-based decision making. The methods involve using NVivo and RapidMiner software to perform lexical analysis, categorization, clustering, pattern recognition, tagging, annotation, memo creation, information extraction, association analysis, and visualization.
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Preethi Kesavan has extensive teaching and learning experience, educational leadership, and executive oversight of strengthening a high-quality student experience, enriching student global mobility, and enhancing academic quality and professional staff effectiveness. She has extensive experience in the Singapore Private Education systems and local public universities, online/mobile/blended learning and professional learning initiatives, quality assurance of curriculum, assessment, and people development and organizational policy development. She is a contemporary architect of flexi-learning, blended learning, e-learning, and education management. She has also designed and implemented IT solutions for the hospitality industry and private schools. She enjoys teaching various programming languages, database and enterprise solutions, and a strong interest in teaching and research activities in spreadsheet modelling and analytics.
This book establishes constructivist, interpretivist, and linguistic approaches based on conventions about the nature of qualitative and text data, the author s influence on text interpretation, and the validity checks used to justify text interpretations. Vast quantities of text and qualitative data in organizations often go unexplored. Text analytics outlined in this book allow readers to understand the process of converting unstructured text data into meaningful data for analysis in order to measure employee opinions, feedback, and reviews through sentiment analysis to support fact-based decision making. The methods involve using NVivo and RapidMiner software to perform lexical analysis, categorization, clustering, pattern recognition, tagging, annotation, memo creation, information extraction, association analysis, and visualization.
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