Science over time: Standing on the shoulders networks

Topic modeling is a technique for discovering the hidden themes or topics that are present in a large collection of text documents. Topic modeling can help researchers and analysts to explore, organize, and understand the content and structure of scientific literature.

What is topic modeling?

Topic modeling is based on the assumption that each document in a corpus (a collection of documents) is composed of a mixture of topics, and each topic is composed of a distribution of words. For example, a document about genetics may have topics such as DNA, genes, mutations, evolution, etc., and each topic may have words such as nucleotide, allele, genome, speciation, etc.

Topic modeling aims to infer the topics and their proportions for each document, and the words and their probabilities for each topic, from the corpus. Topic modeling does not require any prior knowledge or labels for the documents or the topics; it can learn them automatically from the data.

There are many types of topic models, such as latent Dirichlet allocation (LDA), latent semantic analysis (LSA), probabilistic latent semantic analysis (PLSA), non-negative matrix factorization (NMF), etc. Each type has its own advantages and disadvantages, and different parameters and assumptions. However, the general idea is to use some mathematical or statistical methods to find the optimal representation of the corpus as a matrix of topics and words, or a matrix of documents and topics.

How does topic modeling function in science analytics?

Topic modeling can function in science analytics in various ways, such as:

• Literature review: Topic modeling can help researchers to conduct a systematic and comprehensive literature review by identifying the main topics and trends in a scientific field or domain. Topic modeling can also help researchers to find relevant papers or sources for their research questions or hypotheses by searching for keywords or topics.
• Bibliometrics: Topic modeling can help analysts to measure and evaluate the impact and quality of scientific publications by analyzing the topics and citations of papers. Topic modeling can also help analysts to identify the most influential authors, journals, institutions, or countries in a scientific field or domain by examining their topic distributions and collaborations.
• Knowledge discovery: Topic modeling can help researchers to discover new knowledge or insights from scientific literature by finding hidden patterns or relationships among topics, words, documents, authors, etc. Topic modeling can also help researchers to generate new research ideas or questions by exploring the gaps or overlaps among topics or fields.