GoEmotions: A Dataset for Fine-Grained Emotion Classification


GoEmotions: A Dataset for Fine-Grained Emotion Classification

GoEmotions: A Dataset for Fine-Grained Emotion Classification

“To enable building broadly representative emotion models, we applied data curation measures to ensure the dataset does not reinforce general, nor emotion-specific, language biases. This was particularly important because Reddit has a known¬†demographic bias leaning towards young male users, which is not reflective of a globally diverse population. The platform also introduces a skew towards¬†toxic, offensive language. To address these concerns, we identified harmful comments using predefined terms for offensive/adult and vulgar content, and for identity and religion, which we used for data filtering and masking. We additionally filtered the data to reduce profanity, limit text length, and balance for represented emotions and sentiments. To avoid over-representation of popular subreddits and to ensure the comments also reflect less active subreddits, we also balanced the data among subreddit communities.

We created a taxonomy seeking to jointly maximize three objectives: (1) provide the greatest coverage of the emotions expressed in Reddit data; (2) provide the greatest coverage of types of emotional expressions; and (3) limit the overall number of emotions and their overlap. Such a taxonomy allows data-driven fine-grained emotion understanding, while also addressing potential data sparsity for some emotions.

Establishing the taxonomy was an iterative process to define and refine the emotion label categories. During the data labeling stages, we considered a total of 56 emotion categories. From this sample, we identified and removed emotions that were scarcely selected by raters, had low interrater agreement due to similarity to other emotions, or were difficult to detect from text. We also added emotions that were frequently suggested by raters and were well represented in the data. Finally, we refined emotion category names to maximize interpretability, leading to high interrater agreement, with 94% of examples having at least two raters agreeing on at least 1 emotion label…”

Source: ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html

November 11, 2021
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