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Zero-shot text classification and evaluation for Sentiment Analysis in German

Zero-shot text classification for Sentiment Analysis in German

This workflow shows how to use Symanto Brain for zero-shot text classification for the task of sentiment classification.


Zero shot classification on the German SB10k sentiment dataset (Cieliebaketal., 2017)This workflow shows how to use the Symanto Brain for zero shot text classification, in this case for the task of sentiment classification.ReferencesMark Cieliebak, Jan Milan Deriu, Dominic Egger, and Fatih Uzdilli. 2017. A Twitter corpus and benchmark resources for German sentiment analysis. In Proceedingsof the International Workshop on Natural Language Processing for Social Media, pages 45–51. Dataset freely available under the CC-BY 4.0 license. Load and preprocess dataset Dataset statistics Shows the statistical properties of thedataset. Zero-shot classification with Symanto BrainPlease note that the zero-shot task parameters (labels, language,etc.) and the authentication key must be introduced inside the Zero-shot component. Specifically at the Authentication and Zero-shotcomponents, respectively. Evaluate the modelCompute a confusion matrix betweenreal and predicted class values andcalculate the related metrics. Node 9sb10kNode 513Node 514Node 518 DuplicateRow Filter CSV Reader Dataset statisticscomponent Scorer Column Appender Zero Shot component ROC Curve Zero shot classification on the German SB10k sentiment dataset (Cieliebaketal., 2017)This workflow shows how to use the Symanto Brain for zero shot text classification, in this case for the task of sentiment classification.ReferencesMark Cieliebak, Jan Milan Deriu, Dominic Egger, and Fatih Uzdilli. 2017. A Twitter corpus and benchmark resources for German sentiment analysis. In Proceedingsof the International Workshop on Natural Language Processing for Social Media, pages 45–51. Dataset freely available under the CC-BY 4.0 license. Load and preprocess dataset Dataset statistics Shows the statistical properties of thedataset. Zero-shot classification with Symanto BrainPlease note that the zero-shot task parameters (labels, language,etc.) and the authentication key must be introduced inside the Zero-shot component. Specifically at the Authentication and Zero-shotcomponents, respectively. Evaluate the modelCompute a confusion matrix betweenreal and predicted class values andcalculate the related metrics. Node 9sb10kNode 513Node 514Node 518 DuplicateRow Filter CSV Reader Dataset statisticscomponent Scorer Column Appender Zero Shot component ROC Curve

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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: mchinea
Created at: 2023-02-17
On NodePit since: 2023-04-27
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: Text classificationSentiment analysisDeep learningMachine learningSentimentNLP

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