APPLICATION OF TEXT MINING TO TECHNOLOGY MANAGEMENT DOMAIN TO EXTRACT TOPICS AND TRENDS

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2022-1-17

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Tekin, Yaşar

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Topic modeling is a widely used technique to extract latent topics from large document collections. One of the most remarkable uses of it is its application to scientific fields. If topic modeling is applied to all articles published in a specific scientific field, it provides an overall view of topics and trends for the time period under consideration. If it is applied to a single conference or journal, it reveals differences from global trends. The most popular method used for topic modeling is Latent Dirichlet Allocation (LDA). Although LDA is used in many different fields, the problems of how to optimize model parameters and how to eliminate topic instability have not been fully solved yet. This thesis consists of two main parts: 1) An empirical investigation is conducted: a) to investigate the level of topic instability in ordered documents, b) to search for methods to eliminate (if not possible, to alleviate) the effects of the topic instability, c) to evaluate the use of word vector representations to optimize LDA parameters. It is found out that: a) the level of instability is high even in ordered documents, b) average scores of replicated topic models can be used to alleviate the effects of topic instability, c) Skip-gram similarity score is an acceptable measure in optimizing LDA parameters. 2) By using the method proposed, topic modeling is applied to Technology Management (TM) domain. Top topics, the most studied industries, the most used methods and surprising topics of TM literature are identified.

Subject Keywords

Technology Management, Topic Modeling, Latent Dirichlet Allocation, Parameter Optimization, Word Vector Representation

URI

https://hdl.handle.net/11511/95456

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Graduate School of Social Sciences, Thesis

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Citation Formats

Y. Tekin, “APPLICATION OF TEXT MINING TO TECHNOLOGY MANAGEMENT DOMAIN TO EXTRACT TOPICS AND TRENDS,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.