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Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores

101
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October 28, 2024
Published Date

Research Abstract & Technology Focus

This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails.
performance metrics multilabel emotion classification comparing
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What is the core focus of the research titled 'Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores'?

This literature focuses on: This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotio...

Are there open-source GitHub repositories related to Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores?

Yes, open-source projects like mattmireles/gemma-tuner-multimodal (Fine-tune Gemma 4 and 3n with audio, images and text on Apple Silicon, using PyTorch and Metal Performance Shaders.) are actively building upon these concepts.

Which startups are commercializing the technology behind Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores?

Products like Pixel are bringing this to market. Their focus is: Scale performance ads without juggling 7 ad platforms.

What other academic literature is closely related to 'Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores'?

Yes, highly correlated activity was mapped. An entry titled 'Evaluation metrics and statistical tests for machine learning' discusses this: AbstractResearch on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not fa...

Are there commercial applications of 'Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'Shopify/liquid: Performance: 53% faster parse+render, 61% fewer allocations' discusses this: "Tobi found dozens of new performance micro-optimizations using a variant of autoresearch, Andrej Karpathy's new system for having a coding agent r...

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