Topic Explorer View
This component generates a view to visualize the output of a topic model. You can use it to browse topics, their terms, semantic/exclusivity scores and documents. It can be deployed as a data app on KNIME Business Hub or KNIME Server. To see how to combine it with other nodes and verified components check the Topic Modeling space.
Topic Extractor and Assigner (STM)
These two twin components can be adopted to train and test a Structural Topic Model (STM) via the KNIME Interactive R Statistics Integration. The component adopts the KNIME Conda Integration to automatically install the environment on the first component execution. To see how to combine it with other nodes and verified components check the Topic Modeling space.
Category: Text Processing
Topic Scorer (Labs)
This component is designed for scoring topics at the output of a topic model. The experimental component measures semantic coherence, exclusivity and neighbor distances of one or multiple models. The component is part of KNIME Labs as it is an experimental implementation. To see how to combine it with other nodes and verified components check the Topic Modeling space.
Category: Text Processing
Verified Component Categories
Verified components are officially released by the KNIME team and are divided into the following categories. Explore all the verified Components in each category on the KNIME Hub.
Automation Components help when a workflow has to be executed in a production environment in an automated fashion - from complex AutoML to simple tricks to increase flexibility and traceability of your workflow.
When dealing with raw data, there are recurrent data manipulation techniques requiring complex workflows. We offer those workflows via Components to quickly clean, rename, filter, and transform raw data columns.
Finance teams across all industries require specific techniques to keep track of the company’s numbers while ensuring precision and compliance. Those time-consuming tasks usually rely on data from different spreadsheets, which can be hard to maintain and update. These Components help build reusable workflows for an efficient financial analysis.
Training performant predictive models often leads to black boxes: data goes in the box, it's processed by nearly incomprehensible algorithms, predictions come out of the box. KNIME offers an extension to be combined with these Components for Machine Learning Interpretability (MLI) and Explainable AI (XAI) use cases.
The text processing Components help you to analyse text documents - from extracting data from biomedical literature, to document preprocessing, through to computing document similarity.
Easily clean, aggregate, visualize, and forecast time series data with this set of Components. Includes options for seasonality visualization and removal, ACF and PACF plots, as well as ARIMA forecasting.
What are Components?
Components are really KNIME nodes that you create with a KNIME workflow, enabling you to easily bundle, reuse, and share functionality. Configuration and widget nodes allow you to create Components that behave just like normal nodes with a logo, a dialogue, and often interactive views. With KNIME Analytics Platform, anyone can create Components and share them, via the KNIME Hub, with the community.Learn More
Community Components Collections
Seasonal reviews of community components are published on KNIME Blog. Components really are KNIME nodes that you create with a KNIME workflow. Note that these components are built by and for the community via the KNIME Hub. These components have not been officially verified by the KNIME team.
Browse all Components
Visit the KNIME Hub to browse all available components and add them to your workflow.
About KNIME Hub
Learn more about the KNIME Hub and how it can help with your data science solutions.
Read the Blog
Learn more on how to build reliable and reusable components with KNIME.