Streaming Sentiment Analysis of Documents using Document Vector Hashing

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.

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