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Table 4 Comparison between other approaches and ours

From: Sentiment analysis classification system using hybrid BERT models

Model

Dataset

Accuracy%

Notes

DistilBERT with emojis

Airlines

83.74

GLG

Crowdflower

80.42

GLG

Apple

86.81

LGL

DistilBERT without emojis

Airlines

83.47

GLG

Crowdflower

79.24

GLG

Apple

88.04

GLG

RoBERTa with emojis

Airlines

86

3G

Crowdflower

82.39

–

Apple

91.72

–

RoBERTa without emojis

Airlines

85.93

GLG

Crowdflower

81.34

3L

Apple

91.72

3G

Indrayuni et al. [49]

Apple products

85.76

SVM + GA

Dang et al. [50] (two classes)

Sentiment140

80

Word embeeding-RNN

Tweets SemEval

85

Word embeeding-RNN

IMDB Movie Reviews (1)

87

Word embeeding-RNN

IMDB Movie Reviews (2)

86

Word embeeding-RNN

Cornell Movie Reviews

76

Word embeeding-RNN

Book Reviews

76

Word embeeding-CNN

Music Reviews

76

TF-IDF-DNN

Tweets Airline

90

Word embeeding-RNN

Kumawat et al. [51]

Twitter US Airline Sentiment

81.2

BERT

80.8

RoBERTa

Xiang [52]

Twitter collection

76.6

BiLSTM(EPA)

airline dataset

82

BiLSTM(P)

IMDB review

82.6

BiLSTM-AT(P)

Shuang [53]

Twitter airlines

83.3

M_ARC

Yelp

79.1

RC

Janjua et al. [54]

Sanders Twitter Corpus (STC)

78.99

MuLeHyABSC + MLP

Twitter Airline Sentiment (TAS)

84.09

First GOP Debate (FGD)

80.38

Apple Twitter Corpus (ATC)

82.37

Stanford Twitter Sentiment (STS)

84.72

Kian [43]

Twitter US Airline Sentiment

85.89

RoBERTa-LSTM

Twitter US Airline Sentiment Augmented

91.37

IMDB

92.96

Sentiment140

89.70

Barakat [45]

Airline

99.78

ULMFit-SVM

Jain [44] (two classes)

Airlinequality Airline Sentiment Data

90.2

CNN-LSTM

Twitter Airline Sentiment Data

91.3

Thapa et al. [55]

Twitter

60

VADER

Reddit

70

Demotte et al. [56]

CrowdFlower US Airline

82.04

GloVe + shallow capsule network with static routing

Twitter Sentiment Gold

86.87