- Type: TableString input of ColumnsThis component requires input of text columns in String format.
This component extracts the most relevant English keywords in a corpus (a collection of documents) using three specific techniques: - Topic Extraction using LDA: this technique collects a set of keywords for each different topic which clusters documents in different groups. - Term Co-Occurrence: this other technique finds pair of keywords which appear together often in different documents. - Max(TF-IDF) measure: a ranking which measures the importance of terms throughout the corpus. This component takes as input a column of Document type (from String to Document node) and it then identifies keywords in the corpus according to the hyper-parameters defined in configuration dialogue. The collected keywords are then provided in three tables at the output, one of each of the three techniques above. The component by default is applying basic text pre-processing (e.g. stopwords and symbols removal) based on the English language. This pre-processing can be deactivated via the dialogue and performed outside of the component when working with other or multiple languages.
- Type: TableLDA TermsOutput of nouns, adjectives and verbs along with weights defined by LDA in a olumn.
- Type: TableTerm Co-Occurrence countOutput of nouns, adjectives and verbs along with counts of terms occurring in corpus.
- Type: TableTF-IDFTable output of terms with highest TF-IDF between all documents.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.4.0
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