Table 7.
Text classification and sentiment analysis paper
| row | Research work | Classification Algorithm | Algorithm Name | Dataset | Evaluation Criteria | Preferred Method | text representation |
|---|---|---|---|---|---|---|---|
| 1 | Aggarwal et al., 2018 [2] | Deep learning | RNN, BERT, LSTM | Book corpus Wikipedia | ACC, F1 R, Roc | Fine-tuned BERT | Word2vec |
| 2 | Abdel-Nasser and Mahmoud 2019 [1] | Sentiment analysis Deep learning ML | Sent WordNet,LSTM, BERT, LR | IMDB | ACC, F1 P, R | BERT | BERT |
| 3 | Rahman et al., 2020 [46] | Deep learning | CONV-LSTM CNN T-LSTM CNN-LSTM | PIDD | Sensitivity specificity ACC learning rate | CONV-LSTM | – |
| 4 | C. Zhou et al., 2015 [80] | Text Classification | CNN, LSTM C-LSTM | SST | ACC | C-LSTM | N-gram Word2vec |
| 5 | Du et al., 2019 [15] | Text Classification | GRU, LSTM, BiGRU | SST2, Yelp | ACC | LSTM | BERT ELMO ULMFIT |
| 6 | Huang & Feng, 2019 [22] | Text Classification | CRNN, QRNN LSTM, RNN | YELP 2013 | ACC | RNN | GLOVE |
| 7 | Jurgovsky et al., 2018 [27] | Sequence classification ML Deep learning | LSTM Random forest LR | credit-card transactions march to May 2015 | ACC, P R, ROC | LSTM | AS-IS VECTOR EMBEDDI NG |
| 8 | Liu et al., 2016 [35] | Text Classification | RNN LSTM | SST1, SST2 IMBD, SUBJ | ACC | Three LSTM | Matrix-Vector NBOW s paragraph vectors |
| 9 | Lewis et al., 2017 [33] | Text Modeling | RNN, LSTM C-LSTM | Wikipedia | ACC | C-LSTM | ONE-HOT |
| 10 | Song et al., 2019 [53] | Sentiment Analysis | BERT-BASE, BERT-LSTM BERT ATTENTION | ABSA(Aspectbased sentiment analysis) SNLi(The Stanford Natural Language Inference) | ACC, F1 | BERT –LSTM | BERT ELMO |
| 11 | Reddy & Delen, 2018 [50] | Deep learning Text Classification | ANN,LSTM GRU, RNN | Cerner HealthFacts EMR database 2000to2015 | ACC,AUC | LSTM | – |
| 12 | F. Zhang et al., 2019 [77] | Text mining ML | NLP,SVM,DT KNN, NB, LR | different causes of accidents | F1 P R | Optimized Ensemble | TF- IDF |
| 13 | W. Zhang et al., 2019 [76] | Deep learning Semantic Analysis | LSTM,Te LSTM Te LSTM+sc | 20newgroup Wiki10 Amazon Semeval07 | MRR MAP ACC | Te LSTM(Topic-Enhanced LSTM neural) + sc(Similarity Constraint) | BOW N-gram |
| 14 | Ghourabi et al., 2020 [18] | Deep learning Traditional,ML SMS Classification | LSTM, CNN, SVM KNN LR RF, DT | Smsspam Indian 2730 SMS | ACC, F1 P, R | CNN-LSTM | Word2vec GlOVE TF-IDF |
| 15 | X. Wang et al., 2016 [66] | Sentiment Analysis Deep learning | CNN, RNN, LSTM GRU | SST1 SST2 MR | ACC | RNN | Embedding matrix ONE HOT Word2vec |
| 16 | X. Zhang & Zhang, 2020 [73] | Sentimental classification | LSTM, NLP RNN | Reuters Twitter | ACC F1 | LSTM | Word2vec |
| 17 | Bahad et al., 2019 [6] | Sentiment and text classification | CNN,RNN,LSTM BI-LSTM,UNI-LSTM | DS1 and DS2 | ACC | BI-LSTM RNN | BOW,TF-IDF GLOVE, word2vec fast text |
| 18 | J. H. Wang et al., 2018 [68] | Sentiment Classification, Deep Learning | LSTM ELMO NB | IMDB, Douban Movies., social media platform PTT in Taiwan | ACC, F1 P, R | LSTM | Word2vec CBOW Skip gram |
| 19 | Gajendran et al., 2020 [17] | Text processing classification | BLSTM(bidirectional long short term memory) CRF(conditional random field), Text mining | Biomedical NER Corpus | F1 P R | B-LSTM | N-gram |
| 20 | Jelodar et al., 2020 [25] | Deep Sentiment Classification | Deep Sentiment Classification | 563,079 COVID-19–related comments | ACC | LSTM | GLOVE |
| 21 | Ombabi et al., 2020 [41] | CNN LSTM SVM | CNN LSTM SVM | corpus for Arabic text; it contains 63,000 books reviews | ACC, P, R | CNN-LSTM | Fast text Skip gram Word2vec |
| 22 | H. Chen et al., 2020 [13] | Text Classification | NB, KNN, LSTM. SVM, ATT- BILSTM | Taiwan Hospital website | F1, P R, ACC | ATT-BI LSTM | BOW TF-IDF |
| 23 | Banerjee et al., 2019 [7] | Text report classification | SVM, Adaboost CNN, HNN DPA, LSTM | Whole Radiology CORPUS 117816 | F1, P R, AUC | DPA-HNN | Word2VEC GLOVE |
| 24 | Nowak et al., 2017 [40] | Text and Sentiment Classification | LSTM, GRU BI-LSTM | Amazon dataset | ACC | BI-LSTM | BOW |
| 25 | Cai et al., 2020 [10] | Text Classification | LR, CNN BI GRU ATT LSTM HBLA | ARXIV ACADEMIC PAPER DATASET REUTERS CORPUS VOLUME | AUC F1 R p | HBLA(Hybrid BERT model incorporates Label semantics via adjective attention) | Word2vec GLOVE |
| 26 | C. Wang et al., 2017 [67] | Sentence Classification, | CNN RNN DNN | MR SST-1 SST-2 SUBJ IMDB | ACC | CONV RNN | GLOVE |
| 27 | Rao and Spasojevic, 2016 [48] | Text classification | RNN LSTM CNN | Twitter, Facebook and Google+ | ACC | LSTM | GLOVE |
| 28 | A. Onan al., 2021 [42] | Sentiment analysis | CNN, RNN.BRNN LSTM | Courps(Massive open online courses (MOOCs) | ACC | LSTM | GLOVE, Fast Text, Word2vec |
| 29 | Alsentzer et al., 2019 [3] | Text mining | BERT Bio Bert Clinical Bert | MIMIC-III v1.4 database PubMed | ACC F1 | BERT | BERT |
| 30 | Sun et al., 2019 [57] | Sentiment Analysis Sentence Classification | BERT LSTM | 5215 sentences, 3862 of which contain a single target, | ACC F1 AUC | BERT | – |
| 31 | Arase & Tsujii, 2021 [4] | Sentence representation | Bert base NLI PPBERT-base | Twitter URL corpus Wikipedia Para-NMT | ACC | PPBERTbase | – |
| 32 | Colón-Ruiz & Segura-Bedmar, 2020 [14] | Sentiment analysis Multi-class text classification Deep learning | CNN LSTM BI LSTM Bert LSTM CNN LSTM | Book corpus | F1 | LSTM-CNN | Word2vec |
| 33 | Kiran et al., 2020 [30] | Sentiment Classification Deep learning | CNN LSTM | SMS Spam, YouTube Spam, Large Movie Review Corpus, SST, Amazon Cellphone & Accessories and Yelp | F1 P R ACC | LSTM-CNN | BOW TF-IDF |
| 34 | Lee & Hsiang, 2020 [32] | Patent classification | BERT LARGE BERT BASE | CELF-IP | F1, P R | fine-tuning BERT l | – |
| 35 | Y. Zhang et al., 2021 [78] | Sentiment analysis | sentient, SVM,CNN LSTM,ATT LSTM, icon | 3000 multiturn English conversations from several websites (e.g., eslfast.com and focus on English | F1 score P R ACC | interactive LSTM | BOW |
| 36 | P. Zhou et al., 2016 [81] | Text Classification | RNN,LSTM B LSTM | SST 1, SST-2 SUBJ, TREC MR | ACC | B- LSTM | GLOVE |
| 37 | H. C. Wang et al., 2020 [69] | Text summarization | LSTM, RNN | Science Direct in 6 years | P, R ACC, F-1 | RNN | word2vec |
| 38 | Song et al., 2019 [54] | Text mining, Abstractive text summarization . | CNN, LSTM LSTM-CNN, LSTM | Stanford natural language process | ROUGE (Recall-Oriented Understudy for Gisting Evaluation | LSTM-CNN | Word2vec |
| 39 | Tomihira et al., 2020 [60] | Sentiment analysis | CNN, LSTM BERT | Emoji OF Twitter English Japanese | ACC P, R F1 | BERT | Word2vec PCA Fast Text |
| 40 | Wahdan et al., 2020 [65] | Text classification- Deep learning, ML | LSTM, CNN RNN, CNN –RNN SVM, LR,NB DT, RF | Arabic text classification | ACC P R F1 | LSTM | – |
| 41 | Razzaghnoori et al., 2018 [49] | Question classification | RNN LSTM | UTQD.2016 contains 1175 Persian questions | ACC | LSTM | Word2vec TF-IDF |
| 42 | Mohasseb et al., 2018 [37] | Question classification Text classification ML | SVM RF | TREC 2007 Question Yahoo Wikipedia | ACC P R F1 | LSTM | BOW |