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

Zero-shot text classification for Sentiment Analysis in English

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

Zero shot classification on the English semeval sentiment dataset (Nakovetal., 2016)This workflow shows how to use the Symanto Brain for zero shot text classification, in this case for the task of sentiment classification.ReferencesPreslav Nakov, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, and Fabrizio Sebastiani. 2016. SemEval2016 task 4: Sentiment analysis in Twitter. In Proceedings of theInternational Workshop on Semantic Evaluation. Dataset freely available under the CC-BY 3.0 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 9semevalNode 513Node 514Node 518 DuplicateRow Filter CSV Reader Dataset statisticscomponent Scorer Column Appender Zero-shot component ROC Curve Zero shot classification on the English semeval sentiment dataset (Nakovetal., 2016)This workflow shows how to use the Symanto Brain for zero shot text classification, in this case for the task of sentiment classification.ReferencesPreslav Nakov, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, and Fabrizio Sebastiani. 2016. SemEval2016 task 4: Sentiment analysis in Twitter. In Proceedings of theInternational Workshop on Semantic Evaluation. Dataset freely available under the CC-BY 3.0 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 9semevalNode 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 LearningZero-shotNLPSentimentSymanto

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