Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning

arXiv:2410.11355v1 Announce Kind: cross
Summary: Labeling datasets is a noteworthy problem in machine studying, each by way of price and time. This analysis, nonetheless, leverages an environment friendly reply. By exploring label propagation in semi-supervised studying, we are able to considerably scale back the variety of labels required in comparison with conventional strategies. We make use of a transductive label propagation methodology based mostly on the manifold assumption for textual content classification. Our strategy makes use of a graph-based methodology to generate pseudo-labels for unlabeled information for the textual content classification activity, that are then used to coach deep neural networks. By extending labels based mostly on cosine proximity inside a nearest neighbor graph from community embeddings, we mix unlabeled information into supervised studying, thereby decreasing labeling prices. Based mostly on earlier successes in different domains, this research builds and evaluates this strategy’s effectiveness in sentiment evaluation, presenting insights into semi-supervised studying.

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#Decreasing #Labeling #Prices #Sentiment #Evaluation #SemiSupervised #Studying


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