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Sentiment_​Deep_​Learning

Sentiment Analysis

This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used network learns a 128 dimensional word embedding followed by an LSTM.

Define Network Reading and Preprocessing Training and Predicting Evaluation Apply DictionaryReadDictionary.tableimdb.tableShape: max number of words per documentsInput: # word in dictionary+2128 unitsActivation function:sigmoidtrain for 3 epochswith Adamprobability topredictionpredict testdata Strings To Document Dictionary Replacer Partitioning Table Reader Table Reader Keras Input Layer Reduce Dictionary Truncate Keras EmbeddingLayer Keras LSTM Layer Keras Dense Layer Keras NetworkLearner Rule Engine Scorer (JavaScript) Keras NetworkExecutor Zero Pad Number To String Define Network Reading and Preprocessing Training and Predicting Evaluation Apply DictionaryReadDictionary.tableimdb.tableShape: max number of words per documentsInput: # word in dictionary+2128 unitsActivation function:sigmoidtrain for 3 epochswith Adamprobability topredictionpredict testdata Strings To Document Dictionary Replacer Partitioning Table Reader Table Reader Keras Input Layer Reduce Dictionary Truncate Keras EmbeddingLayer Keras LSTM Layer Keras Dense Layer Keras NetworkLearner Rule Engine Scorer (JavaScript) Keras NetworkExecutor Zero Pad Number To String

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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: 2020-06-24
Last update: 2026-01-08
Created with KNIME version: v4.1.3
Tags: deep learning keras text classification classification lstm embedding text analysis sequence analysis sentiment analysis sequence classification neural network text processing

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