This workflows shows an alternative way to execute the Sentiment Analysis example with streaming enabled using the Document Vector Hashing node. The node creates document vectors with a fixed number of dimensions using various hashing methods.
It reads textual data from a csv file and converts the strings into documents, which are then preprocessed, i.e. filtered and stemmed and transformed into numerical/binary document vectors in a streaming fashion. All the preprocessing steps take place in the Streaming text preprocessing meta node. After the document vectors have been created the sentiment class is extracted and a predictive model is built and scored.
Created with KNIME Analytics Platform version (unknown version)