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AI-GENERATED ABSTRACT: Abstract: Few businesses operate in todays' environment without planned and purposeful Enterprise Architecture (EA). As organizations continue to transform and move toward digital, EAs must work with the business to define priorities and align business requirements to IT strategies. Utilizing the outcomes of Koc and others (2021), this paper will use topic modeling to identify, analyze, and report patterns within a dataset (Gillies and others, 2022). This study investigates the application of topic modeling techniques to analyze the goals and priorities of enterprise architects. The analysis below employs LDA, CTM, and BTM models, but there are other topic modeling techniques that could be explored in future research, such as Hierarchical Dirichlet Process (HDP) or Structural Topic Model (STM). Findings from this study provide a foundation for future research and further refinement of topic modeling techniques. Keywords: Enterprise Architecture, digital transformation, business alignment, IT strategy, topic modeling, LDA, CTM, BTM, HDP, STM
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DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business and Management, Berlin International University of Applied Sciences, 2023.
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MACHINE-GENERATED CONTENTS NOTE: Table of Contents abstract........................................................................iv i. Introduction.............................................................1 ii. Literature Review..................................................2 A. Enterprise Architecture and 4em...........................................2 B. Previous Studies in Enterprise Architecture.................................4 C. Nlp and Topic Modeling...................................................5 1. Latent Dirichlet Allocation (lda)......................................5 2. Correlated Topic Model (ctm).......................................6 3. Biterm Topic Model (btm)............................................6 iii. Methodology........................................................7 A. Data Collection...............................................................8 1. Importing the Basic Libraries.........................................8 2. Extracting the Goals....................................................9 B. Data Preprocessing..........................................................9 1. Tokenization.............................................................9 2. Processing With Spacy...............................................10 3. Custom Stop Word Removal Function.................................10 4. Removal of Underscore Character..................................11 5. Lemmatization..........................................................11 6. Applying the Preprocessing Function.................................11 7. Training a Bigram Model............................................11 8. Execution of Final Preprocessing Function..........................12 C. Setting up Lda Model....................................................12 1. the Gensim/corpora Module.......................................12 2. Lda Model Configuration...........................................12 3. Lda Model Stability Test............................................14 4. Lda Model Topic Visualizations..................................14 D. Setting up Ctm Model....................................................15 1. the Tomotopy Module................................................15 2. Ctm Model Configuration..........................................15 3. Ctm Model Stability................................................16 4. Ctm Topics Charts...................................................16 E. Setting up Btm Model....................................................16 1. the Biterm Modules and Preparation.................................16 2. the Btm Configuration..............................................16 3. the Btm Visualizations..............................................17 4. Btm Nan/zero-sum Checks........................................17 iv. Results...................................................................18 A. Modeled Topics............................................................18 1. Lda Output.............................................................18 2. the Ctm Results.......................................................21 3. Btm Results............................................................22 B. Cumulative Findings......................................................23 volume Discussion.................................................................25 A. Validation of the Results................................................25 B. Methodological Reflections..............................................26 C. Suggestions for Future Research.......................................27 vi. Conclusion..............................................................28 vii. References............................................................29 viii. Appendices............................................................33 A. Pyldavis Outputs........................................................33 1. Cluster 1.................................................................33 2. Cluster 2.................................................................33 3. Cluster 3.................................................................34 4. Cluster 4.................................................................34 5. Cluster 5.................................................................35 B. Complete Jupyter Notebook...........................................36 C. Thesis Declaration Page.................................................43
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