REVIEW 2 cited by
XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. We use Plutchik's core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.
Forward citations
Cited by 2 Pith papers
-
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment
SentiXRL is an LLM prompting and self-negotiation framework claimed to improve fine-grained emotion classification on Chinese and English benchmarks, but reported gains are small and internally inconsistent.
-
Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities
A broad survey of emotion recognition and generation across three modalities, but its reliability is hampered by misreported comparative results and citation errors.
Discussion (0). Continue with ORCID to comment.