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Sentiment_​Analysis_​Training

Sentiment Analysis with LSTM - Training

This workflow shows how to train an LSTM neural network for text classification based on the example of sentiment analysis.

Define Network Training and Predicting Evaluation Reading and Preprocessing Shape: max number of words per documentsInput: # word in dictionary + 1128 unitsActivation function:sigmoidTrain the networkProbability topredictionPredict test dataSave trained model Keras Input Layer Keras EmbeddingLayer Keras LSTM Layer Keras Dense Layer Keras NetworkLearner Rule Engine Keras NetworkExecutor Scorer Keras NetworkWriter Read andpartition data Preprocesstraining set Preprocess test set Define Network Training and Predicting Evaluation Reading and Preprocessing Shape: max number of words per documentsInput: # word in dictionary + 1128 unitsActivation function:sigmoidTrain the networkProbability topredictionPredict test dataSave trained model Keras Input Layer Keras EmbeddingLayer Keras LSTM Layer Keras Dense Layer Keras NetworkLearner Rule Engine Keras NetworkExecutor Scorer Keras NetworkWriter Read andpartition data Preprocesstraining set Preprocess test set

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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: kathrinmelcher
Created at: 2018-06-20
On NodePit since: 2020-12-17
Last update: 2024-04-27
Created with KNIME version: v4.2.2
Tags: deep learningkerastext classificationclassificationlstmembeddingtext analysissequence analysissentiment analysissequence classificationneural networktext processing

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