## 5 Materials and Methods
### Latent Dirichlet Allocation (LDA) Model Setting  
Topic modeling is an active domain of Natural Language Processing (NLP) research that can be described as an unsupervised machine learning technique used to find topics in a given text. Its task is to discover the meaning of a given text without having to study it as a whole, i.e., to find the semantic meaning in the content of the text. The most popular algorithms used in topic modeling are Latent Semantic Analysis, Non-Negative Matrix Factorization, Probabilistic Latent Semantic Analysis, and Latent Dirichlet Allocation. Latent Dirichlet Allocation (LDA), applied in the study, is an algorithm that creates groups of words (topics) on the basis of their co-occurrence in a text and then assigns a given label to the created topic with a certain probability. The model is named after a German mathematician of French origin, [PERSON] (1805-1859). [PERSON]'s scientific achievements were used in 2003 by [PERSON], [PERSON], and [PERSON] [47] to conduct topic analysis using Machine Learning techniques, which turned out to be the beginning of a trend that influenced the increased popularity of using the model in topic analysis. LDA is a generative probabilistic model that assumes each document is a mixture of a certain number of topics, and each topic is characterized by a distribution of words. It allowed us to understand the hidden thematic structure in the document collection, thereby revealing the key themes in the procurement of university buildings related to energy efficiency. In this study, we employed Latent Dirichlet Allocation (LDA) as a topic modeling technique to analyze the energy efficiency practices in university buildings. LDA is built upon several key assumptions that guide the topic modeling process and enable us to uncover the underlying thematic structure within the procurement documents. These assumptions played a crucial role in our analysis and interpretation of the topics related to energy efficiency. Firstly, we leveraged the assumption of thematic similarity, which posits that documents discussing similar topics tend to use similar word groups. By applying LDA, we aimed to identify common word groups within the documents that reflected shared themes related to energy efficiency in university buildings. This allowed us to uncover the latent thematic structure and understand the key topics prevalent in the procurement documents. Secondly, we relied on the assumption of word groupings, which suggests that words frequently appearing together within documents indicate their association with a specific topic. Through LDA, we identified these cohesive word groups, enabling us to delineate distinct topics within the corpus. This assumption helped us to uncover the semantic relationships and dependencies among words, facilitating a more comprehensive understanding of the energy efficiency discourse. Furthermore, we incorporated the assumption of a latent document-topic distribution. This assumption suggests that each document exhibits a probability distribution of belonging to different topics. By inferring this distribution using LDA, we gained insight into the prevalence and importance of various energy efficiency topics within individual documents and across the entire corpus. This allowed us to discern the thematic composition of the procurement documents and assess the relative emphasis on different energy efficiency aspects. Lastly, we considered the assumption of a latent word-topic distribution. This implies that each word has a probability of belonging to different topics. By estimating this distribution using LDA, we could determine the topic proportions for each word, facilitating the inference of topics within the documents. This assumption enabled us to capture the variability of word usage across different energy efficiency themes and enhanced our understanding of the nuanced topics discussed in the procurement documents.