AVOBMAT (Analysis and Visualization of Bibliographic Metadata and Text) is a multilingual text mining tool designed for researchers, educators, and students working with large-scale textual and bibliographic data. It combines bibliographic data and textual analysis in a single, integrated and user-friendly web application, enabling users to address complex research questions.
With support for 16 languages, customizable workflows, and transformer-based machine learning technology, AVOBMAT facilitates precise analysis and enrichment of texts and metadata at scale. Users can also share their private databases, fostering collaboration and knowledge exchange.
By streamlining text mining processes, AVOBMAT empowers researchers to focus on critical interpretation and data-driven discovery.
Access public databases or upload yours easily in various formats.
Clean your texts and configure the settings for all the analyses.
Interactive analysis and visualizations include topic modelling, Part-of-speech tagging, Named entity recognition, disambiguation and linking.
Export and import configuration settings. Save statistical data and visualizations. Share or make your databases public.
AVOBMAT can preprocess, analyse, and (semantically) enrich a large number of texts and metadata in several languages without coding skills.
The analytical and visualization tools provide interactive close and distant reading of texts and bibliographic data.
AVOBMAT has a transparent, peer-reviewed workflow with customizable parameter settings for all tools.
AVOBMAT combines bibliographic data and natural language processing research methods (e.g. transformer models) in an integrated, interactive and user-friendly web application.
AVOBMAT fosters critical analysis, for instance, by identifying data gaps and missing metadata values.
AVOBMAT offers multilingual comparative analysis with time-based components.
Researchers, students, teachers of digital humanities, and libraries.
How can AVOBMAT help researchers, libraries and institutions that support or conduct digital humanities research and teaching?
...your or your library’s large digital collections in innovative and interactive ways with customizable preprocessing, analysis & visualization tools;
...by combining bibliographical data and text analyses and using NLP techniques
...unveil overlooked connections, themes, trends & patterns
...and interpret texts, (meta)data & visualizations
(e.g. selection, metadata, classification) in your databases at scale to make more informed decisions about your research (questions)
...novel types of evidence and test old hypotheses
...digital humanities and highlight the challenges, limitations and strengths of computational text analysis and visual representation of digital texts and datasets
...with your colleagues, share your findings and make your databases public