Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets

Table 3

Comparison of our method with existing machine learning algorithms in terms of classification performance (%).

Algorithms	Ft				da				ds
	P	R	F	A	P	R	F	A	P	R	F	A
SVM + Linear	67.1	62.2	62.2	63.9	57.3	44.0	44.0	47.2	63.5	54.1	54.1	56.3
SVM + RBF	70.2	51.0	51.0	40.2	58.5	53.9	53.9	55.5	63.2	51.5	51.5	54.6
XGBoost	69.0	69.5	69.5	66.7	58.3	59.8	59.8	56.3	61.5	62.3	62.3	58.9
ANN	63.1	63.7	63.7	63.4	56.8	58.8	58.8	54.7	62.0	61.9	61.9	58.0
RF	69.6	67.5	67.5	63.5	60.8	60.7	60.7	57.0	59.9	61.9	61.9	59.5
NB	59.4	56.1	56.1	57.5	47.0	48.7	48.7	44.9	48.5	50.0	50.0	45.8
LR	65.1	67.4	67.4	64.7	54.2	56.6	56.6	52.0	63.2	61.8	61.8	61.8
K-NN	65.2	65.2	65.2	60.3	51.8	57.5	57.5	52.8	61.3	61.6	61.6	57.4

Note that P, R, F, and A denote overall Precision, Recall, F1-score, and Accuracy for three types of embeddings (ft: fastText, da: domain-agnostic, and ds: domain-specific), respectively. The hyperparameters of traditional machine learning algorithms are as follows: SVM + Linear (c: 1, Gamma: 0.1), SVM + RBF (c: 100, Gamma: 0.1), XGBoost (learning-rate: 0.1, max-depth: 7, n-estimators: 150), ANN (Hidden-layer-size: 20, learning-rate-init: 0.01, max-iter: 1000), RF (min-sample-leaf: 3, min-sample-split: 6, n-estimators: 200), LR (C: 10, solver: lbfgs, max-iter: 1000), and K-NN (leaf-size: 35, n-neighbor: 120, p: 1). Boldface denotes the highest performance.