## 2 Materials and Methods
### Literature Review
#### 2.1.2 Topic Modeling  
Topic modeling is a text analytics approach based on algorithms that enable the discovery of latent themes in large collections of text documents [19]. These algorithms identify the key terms associated with the different topics and organize the collection of documents according to their associated topics. The topical organization of the documents allows for the application of various text-mining tasks such as document classification, summarization, trend detection, and corpus exploration [19]. It is important to point out that topic modeling does not require any prior annotations or labeling of the documents. The topics emerge from the analysis of the texts, including the original collection of documents.  
The premise of topic modeling is that the documents could be considered as mixtures of latent topics, where each topic is characterized by a distribution over words. Several algorithms have been put forth to build viable topic models. Two of the most representative examples are Latent Semantic Indexing and Latent Dirichlet Allocation. The latter model, often referred to as LDA, has become a standard tool for topic identification [19]. An LDA-based topic model is a probabilistic generative model that has been used widely in recent years with a specific focus on text mining and information retrieval. It has received significant attention and gained widespread interest among researchers in many research fields, including management studies [20].  
The purpose of LDA is to determine the representation of a fixed number of topics in a corpus of documents and, given this number of topics, define the topic distribution within each of the documents in the corpus. The way LDA learns how topics and documents are represented could be intuitively summarized as follows [21] (Figure 1).  
First, the researcher selects the number of topics to be discovered. This step is similar to the first step in cluster analysis, when the researcher needs to specify the number of clusters to be considered. Once the number of topics is selected, the LDA algorithm goes through each of the words in each of the documents and randomly assigns each word to one of the topics. The result of this step is a first representation of the topics in terms of words (specific distribution of the words in each topic) and a representation of the documents in terms of topics. It is important to point out that the assignment of words to topics is carried out in a purely random manner. In this sense, this initial representation is not accurate and needs to be further improved. To improve the initial (inaccurate) representation, the LDA algorithm performs multiple iterations by re-assigning each of the words to the topic that is most highly associated with it: (a) for every single document, it calculates the percentage of words within it that were assigned to a particular topic; (b) for each word in the document, it calculates the percentage of times that particular word has been assigned to a particular topic across all documents; (c) for every word, multiplies the values obtained in (a) and (b) to obtain the probability (degree) of its association to a particular topic; (d) for every word, compares the values obtained in (c) to determine the set of words which are most highly associated with every single topic (this is how the word constitution of each topic is defined); uses a similar logic to determine the set of documents which are most highly associated with every single topic. The key outcomes of the LDA approach are the combination of words and the set of documents that are most highly associated with each of the topics (Figure 1). These results need to be further analyzed to articulate the theme associated with each of the topics. This is performed by the researcher, who needs to go deeper into the interpretation of the topic by examining the most highly associated documents and identifying specific themes emerging in association with each of the topics.