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BERT_​Sentiment_​Analysis_​with_​BERT_​extension

Sentiment Analysis with BERT extension by Redfield

This workflow demonstrates how to do sentiment analysis with BERT extension for Knime by Redfield.

The dataset used here consists of the first 10000 reviews in the IMDB Movie Reviews dataset (http://ai.stanford.edu/~amaas/data/sentiment/) from "Learning Word Vectors for Sentiment Analysis" by Maas et al.

70 / 30training + validation / testWithout fine tuningWithout fine tuningBERT modelcan be cached on diskMeasure nodeexecution timeWith fine tuningWith fine tuningreplace "<br />" with "" and make texts lower case90 / 10training / validationCSV Reader Partitioning BERT Predictor BERT ClassificationLearner BERT Model Selector Models comparison Timer Info BERT Predictor BERT ClassificationLearner String Manipulation Partitioning 70 / 30training + validation / testWithout fine tuningWithout fine tuningBERT modelcan be cached on diskMeasure nodeexecution timeWith fine tuningWith fine tuningreplace "<br />" with "" and make texts lower case90 / 10training / validationCSV Reader Partitioning BERT Predictor BERT ClassificationLearner BERT Model Selector Models comparison Timer Info BERT Predictor BERT ClassificationLearner String Manipulation Partitioning

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: franziska
Created at: 2020-06-25
On NodePit since: 2021-11-16
Last update: 2024-11-17
Created with KNIME version: v4.6.1
Tags: Deep LearningNLPMachine LearningText ProcessingText ClassificationSentiment AnalysisBERTTensorFlowTensorFlow 2PythonTensorFlow HubRedfield

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