| Ref | SA task | Ap | Algorithm | SA level | DS/size | Domain | Language | Features | Accuracy (%) |
| [17] | AD | Un | Syntax-based approach | Aspect | 15000, 15000 | Hotel, products | MSA, DA | | 65.32 | [28] | BR&SC | Su | SVM | Aspect | 5K | Airline | MSA, DA | Skip-gram, FastText | 89 | [34] | BR&SC | Su | SVM | | 363 | | MSA, DA | | 76.09 | [25] | BR&SC | Su | KNN, NB, SVM | | 3015 | Food, sports, weather | MSA, DA | | 76.33 | [109] | SC | Su | Bagging, NB, KNN, DT | | D10, D11, D12 | | MSA, DA | SMOTE | 74.37;74.9, 84.02 | [40] | BR&SC | LB | Corpus and LB approach | Word | 15,274 | 13 domains | MSA, DA, CA | TF-IDF, N-gram | 93 | [43] | BR&SC | Su | SVM | Sentence | D23, D05, D03, D17, D20 | | MSA, DA | TF-IDF, LSA, CBOW, SG | 83.02 | [110] | BR&SC | Su | DCNN | Sentence | 2390 | News | MSA | Word2ve, N-gram | 69.9 | [44] | BR&SC | Su | SVM, LGR | | 183531 | Products | MSA | BoW, TF-IDF | Bal:77.76 Unbal:91.21 | [16] | BR&SC | Su | NB, SVM, RFT | | 10254 | Elections | MSA, DA | N-gram, TF, TF-IDF | 77 | [49] | BR&SC | Su | SVM, KNN, BNB | | 5986 | | MSA, DA | N-gram, TF, TF-IDF | F-m = 88.8 | [45] | SC | Su | RFT, GNB, LGR, SGD | | D09, D05 | | MSA, DA | CBOW, SG | 87.10 | [53] | SC | Hb | Bagging, SVM, RFT, NB | | D10, D11 | | MSA, DA | Unigram, bigram | 90.4; 90 | [55] | SC | LB | SVM, ME, Bagging, Boosting, RFT, NNET, DT, NB | | D16, D03, lex = 5376 | | | | 95.98 | [111] | SC | Su | NB, NB-MLP | | 2154; 13420; 1353; 3962; 8522 | Attraction, hotel, movies, products, restaurants | MSA, DA | | 99.8; 85.1; 95.4; 97.3; 93.1 | [50] | BR&SC | Su | NB, SVM | | 48 | Stock market | MSA, DA | N-gram, TF-IDF, BTO | 83.58 | [51] | BR&SC | Hb | LB, LSTM, CNN, SVM, LGR, NB, DT, RFT | | D24, D05 | | MSA, DA | TF-IDF, CBOW | 87.5; 81 | [57] | Subj C | Su | DL | | 1100 | | MSA, DA | TF-IDF | 92.96 | [65] | SC | Su | NB, SVM | | D09 | Books | MSA, DA | TF-IDF, N-gram | 90.98 | [67] | BR&SC | Su | NB, SVM | | | News for multidomains | MSA, DA | Unigram, bigram, TF, TF-IDF | 84,56 | [112] | SC | Su | CNN-LSTM | Character, Ch5Gram, Word | D01, D03, D05 | Health | MSA | Trigram, ReLU | 95.68; 77.62 94.24; 88.10 | [69] | BR&SC | Hb | SO-NB | Sentence | 1200 | | MSA | N-gram models, semantic features | 90 | [79] | BR&SC | Hb | LGR, PAG, SVM, PRN, RFT, ABT, LB | Review | D13 | Hotel | MSA, DA | Unigram, bigram, semantic features, BOW | 94 to 97 | [113] | Aspect SA | Su | NB, BYN, DT, KNN, SVM | Sentence | D19 | Hotel | MSA | Morphological, syntactic, semantic features | 95.4 | [56] | BR&SC | Su | NB, SVM, DT, RFT | | 1543347; 1462 | Politics | MSA | Syntactic, surface-form, sentiment features | 71.95 | [47] | BR&SC | Su | NB | Sentence | 18278 | Elections | MSA, DA | Unigram, bigram, IG | 93.13 | [59] | BR&SC | Su | SVM, NB | Document | D21 | News, politics, sports, culture | MSA, DA | Unigram, bigram | 90.20 | [46] | BR&SC | Hb | SO, SVM, NB | Sentence | 1520 | | DA | N-gram, CountVectorizer | 92.98 | [114] | SC | Su | CNN | Sentence | D05, D20 | | MSA, DA | Glove, SG, CBOW, random word vectors | 72.1463 | [60] | BR&SC | Su | SVM, NB, KNN | Sentence | 996 | Multidomain | MSA, DA | Unigram, bigram, BTO, TF-IDF | 78 | [73] | SC | Su | DT | Document | D16, D06 | Movies, hotel | MSA, DA | | 93.83; 90.63 | [80] | Subj C, &SC | Hb | DT, NB, KNN, Ontology Baseline, LB | Aspect | D09, 2000 | Books, Hotel | MSA | Domain features | f-m = 79.18;78.83 | [81] | BR&SC | Hb | SVM, NB, KNN | Sentence | 3476 | Movies, economy, sports, history, politics | MSA, DA | | 97.44 | [61] | BR&SC | Su | SVM, KNN, NB, DT, LEM2 | Document | 4812 | | DA | Rough set method | 74 | [66] | BR&SC | Su | RNTN | Phrase, sentence | 1177 | | MSA | Orthographic, morphological features | 80 | [35] | SC | Su | SVM, NB, MNB, SGD, DT | Review | D09 | Books | MSA, DA | Unigram, bigram, TF-IDF, genetic algorithm | 94 | [32] | BR&SC | Un | LB | Document | 1000, 1000 | News, arts | DA | | 73–96 | [15] | BR&SC | Su | SVM | Sentence, document | D19 | Hotel | MSA | Unigram | 76.42 | [83] | BR&SC | Hb | KNN, SVM, LLR, NB, NEUNET | Document | 8861 | 15 domains | DA | Polarity scores, dialects, synsets, inflected forms | 97.8 | [84] | Aspect SA | Un | LB | Review | 200 | | MSA | Sentiment features | 92.15 | [86] | BR&SC | Hb | SVM, NB, LB | Sentence | 64342 | | DA | N-gram | 70 | [115] | SC | Su | Stacking, SGD, RFT, LGR, GNB | Sentence | 1350 | | DA | Word2vec, SMOTE | 85.28 | [54] | SC | Su | SVM, NB | Sentence | 9096 | | DA | BOW, bigram, trigram | f-m = 73 | [68] | BR&SC | Su | SVM, NB | Sentence | 1800 | | MSA, DA | N-gram models, TF-IDF | 88.72 | [88] | SC | Su | MLP, LGR | Sentence | D09 | | MSA, DA | Doc2vec | 32.38 | [90] | SC | Su | CNN, LSTM, RNN | Document | D05, D03 | | DA | CBOW, SG, mul, CONC | 81.63; 87.27 | [82] | SC | Su | SVM, DT, NB, KNN, HCs | Document | D09 | | MSA, DA | BOW, correlation analysis | 72.64 | [48] | BR&SC | Su | DT, RFT, SVM | Sentence | 10254 | Politics | MSA, DA | N-gram models, TF-IDF, TF | 81 | [91] | SC | Su | DNN (LSTM), RNN | Sentence | D09 | | MSA, DA | Word embedding, BOW | 71 | [14] | SC | Su | RNN, SVM | Sentence | D19 | | MSA | word2vec, lexical, morphological, semantic features, N-gram | 95.4 | [116] | Subj C&SC | Su | SVM | Sentence | D04 | | MSA | | 69.37 | [104] | BR&SC | Su | SVM, NB | Sentence | 1121 | Education | MSA | N-gram | 84.62 | [71] | SC | Su | SVM, NB | Review | D16 | Movies | MSA | N-gram models, TF-IDF, BTO, TF | 96.67 | [29] | SC | Su | SVM, LGD | Review | Unbal, bal (D09) | Books | MSA, DA | BOW, N-gram, | 88.51, 78.14 | [30] | BR&SC | Su | LGR, PAG, SVM, PRN | Review | Unbal, bale (D07) | Books | MSA, DA | Unigram, bigram | 0.744–0.911; 0.847–0.85 | [13] | BR&SC | Su | SVM, NB | Document | 1331 | Products | MSA, DA | N-gram, BTO; TF-IDF | 89.68 | [89] | SC | Su | SVM.NB, KNN, ANN | Sentence | 500 | Hotel | MSA | | f-M = 92 | [36] | BR&SC, Subj C | Su | SVM, NB | Sentence | 3700 | | MSA, DA | N-gram models | 89.55 | [58] | SC | Su | AR | | D16 | Movies | MSA, DA | IG, chi-square, GI | 86.81 | [93] | BR&SC | Su | Lexical semantic, CRF | Sentence | 381 | News | MSA | Position, bigram, trigram, morphological | f-m = 84.93 | [94] | BR&SC | Su | SVM, NB, KNN | Document | 250 | Products | MSA, DA | N-gram models | 94 | [95] | BR&SC | Su | SVM, MNB | Sentence | 134194 | | MSA, DA | Automatic labeling | 75.7 | [117] | SC | Su | Rule-based approach | Sentence | D16, 2000 | Movies, arts, politics | MSA, DA | | 85.6, 93.9 | [97] | BR&SC, Subj C | Su | SVM, NB, DT, RFT | Sentence | | Politics | MSA | N-gram, TF-IDF | P = 70.97 | [98] | SC | Su | SVM, KNN, NB, DT, RFT | Sentence | | Terrorism | MSA | Lexical, surface-form, N-gram | P = 71.76 | [99] | SC | Su | SVM | Aspect | Restaurant reviews, 1000, D16, | Novels, products, movies, sports, hotels, restaurants | MSA | N-gram | 85.35 90.60 96.00 | [100] | BR&SC | Su | SVM, BPNN, NB, DT | Sentence | 2000 | | MSA, DA | | 96.06 | [62] | BR&SC | Su | SVM, NB | Sentence | 18278 | Politics | DA | N-gram, TF-IDF | | [118] | SC | Su | SVM, CNB, MNB | Sentence | D10, D11, D12 | | MSA | N-gram, TF, IDF, TF-IDF, IG | 77.34 | [37] | BR&SC | Hb | SO-SVM-NB | Sentence | 4800 | News | DA | N-gram models | 80.9 | [92] | SC | Su | SVM, NB, KNN | Sentence | 3073 | Politics, arts | MSA, DA | N-gram | 68.69 | [74] | BR&SC | Su | SVM, NB, KNN | Sentence | 2,591 | Education, sports, politics | MSA | BTO, TF-IDF, TF | 69.97 | [63] | BR&SC | LB | LB | Sentence, document | D09, D14 | Health, book | MSA, DA | | 71 | [38] | BR&SC | Su | NB, SVM, ME | Document | 28576 | | DA | BoW, N-gram models | 86.75 | [41] | SC | Su | SVM, NB, DT | Sentence | 2000 | Politics, arts | MSA | | P = 80.9 | [101] | BR&SC | Hb | LB, NB | Sentence | 2590 | Restaurants | DA | Sentiment words | 90.54 | [102] | BR&SC | Su | NB, DT | Sentence | 2000 | | MSA | | 64.85 | [33] | BR,&SC, Subj C | Su | RFT, GNB, SVM, LGR, SGD | Word | D05, D03, D17, D09 | Quran, customers | MSA, DA | CBOW | 80.21–81.69 | [119] | SC | LB | Rule-based, LB | | D16 | | MSA | | 89.6 | [18] | BR&SC | Su | CRF, DT, NB, KNN | Sentence | 2265 | News | MSA | NER, N-gram | 86.5 | [42] | BR&SC | Hb | SO, SVM | Sentence | 1103 | | MSA, DA | N-gram models | 84.01 | [103] | BR&SC | LB | LB | Word | D25, D05, D18, Lex: D25-PMI | | DA | | 89.58 | [87] | BR&SC | Su | SVM | Sentence | 625 | Multidomain | MSA, DA | | 83.5 | [96] | BR&SC | Su | Bagging, boosting | Sentence | 1500 | Sports, news, Economics | MSA, DA | | 85.95 | [72] | BR&SC | Su | SVM, NB | Document | D02 | | MSA, DA | | M-P = 90.5 | [31] | BR&SC | Su | SVM-KNN | Review | 625 | Hotel | DA | | 97 | [105] | SC | Su | DT, Dtable, SVM, MNB, voting (KNN, DT, and NB) | Sentence | Unbal, bal : D09 | | MSA | BOW | 42.7–46.4 | [120] | BR,&SC, Subj C | Su | SVM, MNB, BNB, PAG, SGD, LGR, PRN, KNN | Sentence | D05 | | DA | N-gram models | 69.1 | [121] | BR&SC | Su | Voting (RSS and SVM) | Sentence | 800 | News | MSA, DA | | 98 | [64] | SC | Su | Voting (ME, SVM, and ANN), bagging, boosting, stacking | Document | D16, D11-D13 | | MSA | Stylistic, morpholexical, tigram, brigram, TF-IDF, TF, BTO | F1 = 85.06 | [122] | BR&SC | Ss | SVM, LGR, BNB, KNN, SGD | Document | D09 | Movies, hotels, restaurants, products | MSA | Unigram, bigram, TF-IDF, word count | 82.4 | [106] | BR&SC | Su | SVM, NB, KNN | Sentence | 500 | | MSA | | f-m = 91.5 | [70] | BR&SC | Su | NB, KNN | Document | 2591 | Education, sports, politics | MSA, DA | Bigram | M-P = 75.25 | [26] | BR&SC | Su | SVM, NB, KNN | Sentence | D16, 164 | Politics | MSA, DA | Correlation, N-gram models | 96.6 | [107] | BR&SC | Su | NB, KNN | Sentence | 300 | | MSA | | 63.79 | [75] | BR&SC | Su | SVM, NB | Sentence | 300, 250 | | DA | N-gram models | 0.75.64 | [123] | SC | Su | SVM, NB | Document | 5070 | News, entertainment, sports, science, business | MSA | Chi-square, correlation, GSS coefficient, IG, relief F | M-F = 95.1 | [76] | BR&SC | Su | NB, voting | Document | 4812 | | DA | Rough set method, genetic | 57 | [52] | BR&SC | Su | SVM, NB | Document | D22, D16, 7400 | | MSA | BTO, TF, TF-IDF, score | 94.88–97.81 | [124] | BR&SC | Ss | Pattern matching, majority with entities | Sentence | 5000 | Restaurants | DA | | 60.5 | [24] | Subj C, and SC | Su | SVM, NB | | Obama, Messi, iPhone, shia | Politics, social, products | MSA | N-gram, IDF, TF-IDF | 68.05; 87.43 | [85] | BR&SC | Su | SVM, MNB | Sentence | 260 | | DA | N-gram | 72.78 | [39] | BR&SC | Su | SVM | Sentence | 1350 | News | DA | | P = 88.63 | [77] | SC | Hb | Voting (SVM, NB, and SO) | Sentence | D16, D22 | | MSA | N-gram models | F1 = 90.74 | [78] | SC | Su | NB, SVM, KNN | Document | D16, D10, D11 | | MSA | TF-IDF, TF, IDF, N-gram | 93.0, 78.2 | [125] | SC | Su | MNB, BNB, SVM | Sentence | Unbal, bal : D09 | | MSA, DA | | 91.0, 82.7 | [27] | BR&SC | Su | SVM, NB | Sentence | Unbal, bal: 4625 | Arts, politics, science, technology, social | MSA, DA | | 68.2, 61.4 | [12] | BR&SC | Su | KNN, NB, DT, SVM | Sentence | 1000 | | MSA | Social mention, Senti. strength | 95.59, 93.29 |
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