wikipedia_id:28934119:en;wikidata_id:Q3532085:en
Introduction: This blog post by Lucy Havens presents a sentiment analysis of over 2000 Times Music Reviews using freely available tools: defoe for building the corpus of reviews, VADER for sentiment analysis and Jupiter Notebooks to provide a rich documentation and to connect the different components of the analysis. The description of the workflow comes with tool and method criticism reflections, including an outlook how to improve and continue to get better and more results.
Introduction: In our guidelines for nominating content, databases are explicitly excluded. However, this database is an exception, which is not due to the burning issue of COVID-19, but to its exemplary variety of digital humanities methods with which the data can be processed.AVOBMAT makes it possible to process 51,000 articles with almost every conceivable approach (Topic Modeling, Network Analysis, N-gram viewer, KWIC analyses, gender analyses, lexical diversity metrics, and so on) and is thus much more than just a simple database – rather, it is a welcome stage for the Who is Who (or What is What?) of OpenMethods.
Introduction: The indispensable Programming Historian comes with an introduction to Term Frequency – Inverse Document Frequency (tf-idf) provided by Matthew J. Lavin. The procedure, concerned with specificity of terms in a document, has its origins in information retrieval, but can be applied as an exploratory tool, finding textual similarity, or as a pre-processing tool for machine learning. It is therefore not only useful for textual scholars, but also for historians working with large collections of text.