Stanford Topic Modelling Toolbox is a free collection of topic modelling tools, and a part of the Stanford Natural Language Processing toolset. It is designed for social scientists and other users who need to analyze datasets with a substantial textual component. This tool can accept and generate tab-separated and comma-separated values, and is designed to work in combination with spreadsheet programs like Excel.

Paper Machines is a topic modelling and visualization tool available as a plugin for Zotero. It analyzes Zotero bibliographic collections based on a selection of text mining processes, and enables users to export a variety of visualizations, such as word clouds, phrase nets or heat maps, from the results.

The DfR Browser is a free, open source visualization interface for exploring aggragates of articles from the JSTOR database. It uses topic modelling, co-occurrances and document metadata to provide multiple views on the corpus of interest based on topic searches, via several specialized views such as scaled, list and yearly subviews. Users can test the DfR browser via the provided web demo, which incudes a broad selection of pre-loaded topics, or download the source code to use the tool with their own data. The tool's output can be imported into Mallet for further analysis.

jsLDA is a free, open source tool for corpus-based in browser topic modelling. Users can test the tool via the provided demo, or download the source code to run on their own system. Users can load any corpus accessible from a URL, and can train the tool on the topics within the corpus via the iteration function. Once jsLDA has been trained to the user's satisfaction, documents can be sorted according to topic and explored based on topic correlations.

The Networked Corpus is a free, open source tool for navigating a corpus of plain text files via topic modelling using MALLET. The tool consistes of a Python script, and generates a collection of HTML files that can be explored further via the user's web browser. To use The Networked Corpus, users must have a minimum of Python 2.7.x, NumPy, SciPy and MALLET 2.0.7 installed on their system.

Topic Modelling Tool is a free, open source tool for Latent Dirichlet Allocation topic modelling with a graphical user interface. The tool learns topics in a user-supplied corpus of plain text files, and outputs results as a CSV file for further analysis.