Table 21.

Best results achieved for each dataset by evaluating combinations of language models and classifiers, regarding Transformer-based models and static word representations

Dataset	Accuracy			$F_1$-macro
	%	Classifier	Model	%	Classifier	Model
iro	80.48	LR	BERT	75.87	LR	Emo2Vec
sar	87.50	LR	SSWE	87.19	LR	SSWE
ntu	95.30	MLP	w2v-Edin	95.19	MLP	w2v-Edin
S15	92.23	LR	BERTweet	83.33	LR	BERTweet
stm	90.79	LR	RoBERTa	90.75	LR	RoBERTa
per	87.69	LR	BERTweet	85.29	LR	BERTweet
hob	94.82	MLP	BERT-static	94.05	MLP	BERT-static
iph	87.59	MLP	BERTweet	84.72	MLP	BERTweet
mov	89.47	MLP	RoBERTa	82.12	LR	BERTweet
san	91.17	MLP	BERTweet	91.11	MLP	BERTweet
Nar	95.60	MLP	BERTweet	95.43	MLP	BERTweet
arc	90.74	MLP	BERTweet	90.51	MLP	BERTweet
S18	88.97	SVM	BERTweet	88.87	SVM	BERTweet
OMD	87.36	SVM	BERTweet	86.40	SVM	BERTweet
HCR	81.55	XGB	BERTweet	76.77	LR	BERTweet
STS	93.90	LR	BERTweet	92.98	LR	BERTweet
SST	86.76	SVM	BERTweet	86.53	SVM	BERTweet
Tar	86.93	SVM	BERTweet	86.92	SVM	BERTweet
Vad	90.80	LR	BERTweet	89.38	LR	BERTweet
S13	89.61	LR	BERTweet	87.37	LR	BERTweet
S17	92.56	SVM	BERTweet	92.08	SVM	BERTweet
S16	91.03	LR	BERTweet	89.05	LR	BERTweet