Date of Award
5-2-2023
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mariofanna Milanova
Abstract
This thesis presents a study on topic modeling and future prediction of aid data in development studies using LDA and BERT. The goal of this study is to explore the aid data from four sectors: Government and civil society, Government and civil society general, Conflict prevention and resolution, peace and security, and Women, and to identify the latent topics that are present in the data. The LDA and BERT algorithms were used for the topic modeling, and coherence scores were computed to evaluate the quality of the models. The results of the study show that the LDA and BERT models were able to identify meaningful and interpretable topics in the aid data. The coherence scores indicated that the BERT model outperformed the LDA model in terms of topic coherence. Moreover, the BERT model was also used to predict future topics in the aid data. The predicted topics could be used to inform policy decisions and aid allocation in the future. Overall, this study provides insights into the use of topic modeling and future prediction of aid data in development studies. The findings of this study could have practical implications for aid organizations and policymakers.
Recommended Citation
Oyshi, Uttamasha Anjally, "Topic Modeling and Future Prediction of Aid Data in Development Studies Using LDA and BERT" (2023). Theses and Dissertations. 1129.
https://research.ualr.edu/etd/1129
