Natural Language Processing Nodes

Analyze data from any source to understand customer attitudes, opinions and emotions about your brand
and offerings. React accordingly in a quick and efficient manner!

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Workflow demonstrating the compatibility of Redfield NLP nodes with existing KNIME nodes

Features and Use Cases

spaCy extension

Text processing
Tokenization:

split the document into words, this is an essential task for text processing

Part-of-speech tagging:

search meaningful parts of the sentence

Named Entity Recognition:

extract entities mentioned in the document

Lemmatization:

transform the words to their root form

Morphology analysis:

understand the relationships between subjects, objects and actions in the sentence

Vectorization:

represent words and documents as vectors so it can be used for machine learning

BERT extension

Process thousands of support conversations, customer reviews or social media posts.
Feed in raw text – no text processing needed!

Text classification:

identify the type or sentiment of the documents

  • Document classification: label documents according to its content
  • Multi-Label document classification: assign different labels to a specific text according to
Aspect Based Sentiment Analysis:

compare customers’ opinions on your products and services with competitors’ by categorizing data by aspects e.g. quality, comfort, battery life, etc (will be released in Q4 2021)

BERT Embeddings and Similarity search:

understand if documents are alike or different

Question answering:

extract an answer from a text without reading it (will be released in 2022)

NLP visualization nodes

Tagged text visualization:

visualize multiple types of tags simultaneously (will be released in 2022)

Morphology analysis visualization:

a graphical representation of the relationships between morphemes of the sentence (will be released in 2022)