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% |