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Table 9 Performance comparison between developed existing and proposed methods

From: Twitter sentiment analysis using hybrid gated attention recurrent network

Ref no and author name

Technique

Dataset

Performance metric

Proposed

GARN

Sentiment140 dataset

Accuracy–97.86%

Alharbi et al. 2019 [16]

CNN

SemEval-2016 1, and SemEval-2016 2

Accuracy–86.48%, precision–88%, recall–89%, and F1-score–87%

Tam et al. 2021 [17]

ConvBiLSTM

Retrieved Tweets and SST-2 datasets

Accuracy–91.13%, precision–94.6%, recall–94.33%, and F1-score–92.08%

Chugh et al. 2021 [18]

DeepRNN-SMCA

Amazon unlocked the mobile reviews dataset, Telecom tweets

Accuracy–97.7%, precision–95.5%, recall–94.6%, and F1-score–96.7%

Alamoudi et al. 2021 [19]

ALBERT

Yelp Dataset

Accuracy–89.49%, precision–89.02%, recall–89.49%, and F1-score–89.21%

Tan et al. 2022 [20]

RoBERTa-LSTM

Sentiment 140 dataset

Accuracy–89.7%, precision–90%, recall–90%, and F1-score–90%

Hasib et al. 2021 [21]

DNN-CNN

CrowdFlower Twitter US Airline Sentiment

Accuracy–91%, precision–85.66%, recall–87.33%, and F1-score–87.66%

Gaye, B et al., 2021 [31]

LR-LSTM

Sentiment 140 dataset

Accuracy–80%, precision–81%, recall–80%, and F1-score–90%

Ahmed, K et al., 2022 [32]

GA(SAE)-SVM

Sentiment 140 dataset

Accuracy–84.5%, precision–84.2%, recall–83.6%, and F1-score–83.9%

Subba, B. and Kumari, S, 2022 [33]

Bi-GRU-LSTM

Sentiment 140 dataset

Accuracy–84%, precision–85%, recall–83%, and F1-score–84%