During the process, only one hyperparameter varied, and the other remained unchanged until reaching the highest coherence score. The search for an optimal number of topics in our study started with a range from two to 15, with a step of one. A grid search was performed for the number of topics (K) as well as for beta and alpha. Starting from LDA, three hyperparameters are required. The main difference between Top2Vec is the application of a class-based c-TF-IDF algorithm, which compares the importance of terms within a cluster and creates term representation. BERTopic also supports UMAP for dimension reduction and HDBSCAN for document clustering. For example, words like “mom” and “dad” should be closer than words like “mom” and “apple.” Since word vectors that emerge closest to the document vectors seem to best describe the topic of the document, the number of documents that can be grouped together represents the number of topics.īERTopic builds upon the mechanisms of Top2Vec and provides document embedding extraction with a sentence-transformers model for more than 50 languages. That is, the vectorization of text data makes it possible to locate semantically similar words, sentences, or documents within spatial proximity. Top2Vec is a comparatively new algorithm that uses word embeddings. W contains the basis vectors, and H contains the corresponding weights. Specifically, NMF decomposes its input, which is a term-document matrix (A), into a product of a terms-topics matrix (W) and a topics-documents matrix (H). NMF works on TF-IDF-transformed data by breaking down a matrix into two lower-ranking matrices. In contrast to LDA, NMF is a decompositional, non-probabilistic algorithm using matrix factorization and belongs to the group of linear-algebraic algorithms. Since the number of topics need not be predefined, LDA provides researchers with an efficient resource to obtain an explicit representation of a document. It is a three-level hierarchical Bayesian model, where each collection item is represented as a finite mixture over an underlying set of topics, and each topic is represented as an infinite mixture over a collection of topic probabilities. LDA is a generative probabilistic model for discrete datasets. To shed light on the efficacy of different algorithms, his article takes tweets with #covidtravel as well as the combination of #covid and #travel as the reference points and evaluates the performance of four topic modeling namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. Yet, methodological challenges emerge in both the data collection and analysis process. In the 21 st century, data-driven approaches provide brand-new perspectives on interpreting a phenomenon. Have you been wondering what would be the best way to analyse short, text-heavy, and unstructured content from social media? Certainly, social media has opened an entirely new path for social science research, especially when it comes to the overlap between human relations and technology. Full paper published by: Roman Egger and Joanne Yu, Frontiers in Sociology,

Lda model coherence score