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Sentiment_​Analysis_​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 Shape: max number of words per documentsInput: # word in dictionary + 1128 unitsActivation function:sigmoidTrain the networkFilter unusedwords#vocab:20000Read input tableReadDictionary.tableApply DictionaryPreprocessing of documentsPredict test data Keras Input Layer Keras EmbeddingLayer Keras LSTM Layer Keras Dense Layer Keras NetworkLearner Scorer (JavaScript) Dictionary Filter Reduce Dictionary Table Reader Zero Pad Truncate Table Reader Dictionary Replacer Strings To Document Partitioning Preprocessing Extract prediction Keras NetworkExecutor Define Network Reading and Preprocessing Training and Predicting Evaluation Shape: max number of words per documentsInput: # word in dictionary + 1128 unitsActivation function:sigmoidTrain the networkFilter unusedwords#vocab:20000Read input tableReadDictionary.tableApply DictionaryPreprocessing of documentsPredict test data Keras Input Layer Keras EmbeddingLayer Keras LSTM Layer Keras Dense Layer Keras NetworkLearner Scorer (JavaScript) Dictionary Filter Reduce Dictionary Table Reader Zero Pad Truncate Table Reader Dictionary Replacer Strings To Document Partitioning Preprocessing Extract prediction Keras NetworkExecutor

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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: 2020-08-15
Last update: 2024-11-21
Created with KNIME version: v4.2.0
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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