The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism
Open accessAbstract
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On the other hand, long-term dependencies between word sequences can be captured by LSTM models, which is a vital requirement in the context of cyberbullying detection [125].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [125]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173 · cited 1×
“The model leverages the novelty of BERT (bidirectional encoder representation from transformers) [31] for text extraction.”
MethodConvolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · cited 2×
“To further increase the sentiment classification accuracy of unstructured text, Yoon Kim [7] proposed a 1D CNN approach for text classification.”
MethodFramewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
A Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · cited 1×
“With the 1D CNN, the convolution layer uses a 1D cross-correlation operation that involves a sliding convolution window with variable-size kernels over the input text starting from left to right [13].”
MethodRecurrent Convolutional Neural Networks for Text Classification
Lai, S., Xu, L., Liu, K., & Zhao, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9513 · Cited by 2,384
Deep learning for sentiment analysis: A survey
Zhang, L., Wang, S., & Liu, B. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1253 · Cited by 2,182 · cited 1×
“Natural-language-based sentiment classification has a wide range of applications, such as movie review classification, subjective and objective sentence classification, and text classification technology [1].”
BackgroundComparative Study of CNN and RNN for Natural Language ProcessingPreprint
Yin, W., Kann, K., Yu, M., & Schütze, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.01923 · Cited by 1,264 · cited 1×
“Figure 2 shows the basic architecture of the 1D CNN [14].”
BackgroundBidirectional LSTM with attention mechanism and convolutional layer for text classification
Liu, G., & Guo, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.078 · Cited by 1,166 · cited 1×
“1D CNN processes text as a one-dimensional image and a 1D CNN is used to capture the latent associations between neighboring words, in contrast with LSTMs, which process each word in a sequential pattern [8].”
MethodA Convolutional Neural Network for Modelling SentencesPreprint
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.2188 · Cited by 967 · cited 1×
“With the 1D CNN, the convolution layer uses a 1D cross-correlation operation that involves a sliding convolution window with variable-size kernels over the input text starting from left to right [13].”
Method
Research works printed but not linked: 2
- Pouyanfar, 2018, A survey on deep learning: Algorithms, techniques, and applications, ACM Comput. Surv. (CSUR), 51, 1
- Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv.
Cited by
Sorted by citations
Items 201–300 of 442
A Comparative Study of Deep Learning Neural Networks in Sentiment Classification from Texts
Tahseen, T., & Kabir, M. M. J. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-7996-4_20 · Cited by 4
Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU
Mouthami, K., Yuvaraj, N. R., Thilaheswaran, K. K., & Lokeshvar, K. J. (2023) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc57686.2023.10193121 · Cited by 5
“Even yet, it was still being determined if sentiment categorization could be used as an appropriate comparison tool between shallow models and deep learning techniques, which have the far greater representational ability [19].”
BackgroundHadrian, A., Vainshtein, R., Shapira, B., & Rokach, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3296532 · Cited by 4
“Recent work [58] showed that CNN-based models (such as FCNs) are capable of extracting higher-level features or patterns in the data, while LSTM models capture long-term dependencies.”
BackgroundFazri, A., Purbolaksono, M. D., & Astuti, W. (2024) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa62899.2024.10651822 · Cited by 4
“2, Continuous Bag of Words (CBOW) deduces the target word when provided with the context word, whereas Skip-Gram deduces the context word when provided with the input word [15].”
BackgroundKan, Y. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425407521 · Cited by 4
Deepika, A., & Radha, N. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-3728-5_47 · Cited by 4
Analysis on Sentiment Analytics Using Deep Learning Techniques
Anusha, M. G., & Leelavathi, R. (2021) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac52330.2021.9640790 · Cited by 4
Jasmir, J., Riyadi, W., Agustini, S. R., Arvita, Y., Meisak, D., & Aryani, L. (2022) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v6i4.4005 · Cited by 4
“Beakcheol Jang[21] used BiLSTM to improve text classification.”
MethodAn Effective Framework for Sentiment Analysis of Hindi Sentiments Using Deep Learning Technique
Shrivash, B. K., Verma, D. K., & Pandey, P. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10702-y · Cited by 4
Understanding and Analyzing Appraisal Systems in the Underground Marketplaces
Li, Z., & Liao, X. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23187 · Cited by 4
“In our implementation, we compare the performance of three DNN-based models (TextCNN [68], LSTM [60], BiLSTM [65]) and six statistical ML models (Support Vector Machine (SVM), Naive Bayesian, Logistic Regression, K-Nearest Neighbors, Multi-Layer Perceptron, and Random Forest), which are widely used…”
MethodBidirectional Deep Learning Decoder for Polar Codes in Flat Fading Channels
Aziz, M. A., Rahman, M. H., Sejan, M. A. S., Tabassum, R., Hwang, D., & Song, H. (2024) · IEEE Access · doi:10.1109/access.2024.3476471 · Cited by 4
“By modeling intricate relationships across entire sequences, Bi-LSTMs excel in tasks that require complex temporal data comprehension [53], [54], [55].”
BackgroundIntelligent construction benefits the public: evidence from the opinion analysis on social media
Wang, L., Yuan, J., Chen, Y., Wan, X., & Huang, G. (2024) · Engineering Construction & Architectural Management · doi:10.1108/ecam-09-2024-1250 · Cited by 3
Boosting Arabic text classification using hybrid deep learning approach
Alnagi, E., Ghnemat, R., & Al-Haija, Q. S. A. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07025-x · Cited by 3
A Novel Approach for Mitigating Class Imbalance in Arabic Text Classification
Nabil, E., Nagib, A. E., Hany, M., Faizullah, S., & Gomaa, W. H. (2025) · IEEE Access · doi:10.1109/access.2025.3604427 · Cited by 4
“Originally intended for image processing, CNNs suited well to text classification by considering text as a one-dimensional string of words or characters, capturing local patterns and relevant structures through convolutional filters [8], [29], [30], [31], [32].”
Khekare, U., & Vedaraj, I. S. R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1700664 · Cited by 6
Karimah, A. F., Afinda, A. M., & Kurniawan, I. (2023) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa58501.2023.10277245 · Cited by 3
“LSTM has three main components in its architecture: the input gate, forget gate, and the output gate [19].”
BackgroundMethodInfluentialAdaptive attention-aware fusion for human-in-the-loop behavioral health detection
Brown, M., Azmee, A. A., Khan, M. A. A. H., Thomas, D. M., Pei, Y., & Nandan, M. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100475 · Cited by 3
Ji, G., Chen, Z., Liu, H., Liu, T., & Wang, B. (2024) · Applied Sciences · doi:10.3390/app14114863 · Cited by 3
Liu, Z., Li, F., Hao, G., He, X., & Zhang, Y. (2024) · Computer Science and Information Systems · doi:10.2298/csis240314049l · Cited by 3
“Jang et al. [20] used word vectors and Bi-LSTM hidden vectors respectively to compute attention iteratively.”
Quality Assessment of Text Data Using C-RNN
Sharaff, A., Khurana, S., & Sahu, T. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-1781-2_19 · Cited by 3
Hybrid recommendation algorithm based on real-valued RBM and CNN
Wu, J., Yang, L., Yang, F., Zhang, P., & Bai, K. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022499 · Cited by 3
Human action recognition from inertial sensors with Transformer
Le, T.-H., Tran, T.-H., & Pham, C. (2022) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr56351.2022.9924794 · Cited by 3
TE-BiLSTM: Improved Transformer and BiLSTM on Fraudulent Phone Text Recognition
Xu, H., Zhou, J., Jiang, T., Lu, J., & Zhang, Z. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-6142-7_1 · Cited by 3
Cheng, T., & Li, L. (2023) · IET Software · doi:10.1049/sfw2.12117 · Cited by 3
RetractedAnalysis Comparison of FastText and Word2vec for Detecting Offensive Language
Lumbantoruan, R., Siregar, R. U., Manik, I., Tambunan, N. D., & Simanjuntak, H. T. A. (2022) · International Conference of Computer Science and Information Technology (ICoSNIKOM) · doi:10.1109/icosnikom56551.2022.10034886 · Cited by 3
Can We Predict Consequences of Cyber Attacks?
Datta, P., Namin, A. S., & Jones, K. S. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00174 · Cited by 3
“Jang et. al [9] present a similar architecture which uses a combination of Bidirectional LSTM (BiLSTM) and CNN with attention mechanism on Internet Movie Database (IMDB) movie review dataset to predict movie genres.”
BackgroundSmitha, E. S., Sendhilkumar, S., & Mahalakshmi, G. S. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-230246 · Cited by 3
Kyume, A., Rahman, M. M., Azad, M. I., Nahid, M. N. I., Khan, M. S. H., & Uddin, M. M. (2023) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora58378.2023.10156660 · Cited by 3
“Bi-LSTM is typically used in situations where sequence-to-sequence activities are required [19] [22] [20] [21].”
BackgroundMethodFederated Learning for Wearable Sensor-Based Human Activity Recognition
Sachin, D. N., Annappa, B., & Ambesenge, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1912-3_12 · Cited by 3
Sarin, G., Kumar, P., & Mukund, M. (2023) · Benchmarking An International Journal · doi:10.1108/bij-07-2022-0454 · Cited by 3
A Comparison of Deep Learning for Software Features Extraction in Forensic Online News
Suarezsaga, F., Siahaan, D., & Yuniarti, A. (2023) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce58721.2023.10237097 · Cited by 3
“These three types of classifier were chosen because, in previous studies, the use of these methods was used in text classification and showed good results [21][25].”
MethodEmotion Recognition from Twitter Comments Using Deep Learning
Mahfouz, K. H., & Al‐Naymat, G. (2023) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit58888.2023.10453708 · Cited by 3
“Literature on text classification does not stop here; among some of what was interesting were ones that implement LSTM models with attention, such as the work done in [12] or ones that combine attention, LSTMs, and CNNs such as [13].”
BackgroundDevelopment of a GUI for Automated Classification of Scientific Journal Articles using clustering
Sateesh, N., Kaur, K., Lakshminarayana, M., Vekariya, V., Patil, H., & Maranan, R. (2024) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit61487.2024.10580528 · Cited by 3
“Currently existing methods imply the availability of pre-prepared data, step-by-step analysis of this data, as well as independent formation of a method for displaying the results [9-10].”
MethodImproving Distantly Supervised Relation Extraction with Multi-Level Noise Reduction
Song, W., & Yang, Z. (2024) · AI · doi:10.3390/ai5030084 · Cited by 3
“Several methods have been proposed for the sake of removing noisy words, such as dependency tree parser [6,7] and word-level attention [8–11] methods.”
Sookarah, D., & Ramwodin, L. S. (2022) · International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM) · doi:10.1109/elecom54934.2022.9965237 · Cited by 3
“The number of epochs is considerably lower when compared to other traditional sequential machine learning models such as LSTM which can vary from a dozen [45] to a few hundred [46] depending on the application and the model configuration.”
MethodIntensity quantification of public opinion and emotion analysis on climate change
Thukral, T., Varshney, A., & Gaur, V. (2021) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2021.874417 · Cited by 3
“Since sentiment classification requires an adequate understanding of the contextual information, algorithms such as LSTM and CNN may be utilized to provide adequately accurate results due to their ability to identify long-term text dependencies [24].”
MethodAn Embedding Method for Sentiment Classification across Multiple Languages
Nguyen, L.-T. D., Nguyen, N. D., & Bui, K. N. (2021) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse53942.2021.9648795 · Cited by 3
Maulana, F., & Asih, T. S. N. (2023) · Journal of Soft Computing Exploration · doi:10.52465/joscex.v4i1.116 · Cited by 3
Improving Audio Anomalies Recognition Using Temporal Convolutional Attention NetworkPreprint
Huang, Q., & Hain, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11286 · Cited by 3
“As a comparison, besides the proposed approach (TCAN), the related experiments were also conducted using another four baseline methods: TCN parallel[16], TCN[6], CNN[20], and, LSTM att[21].”
Kumar, V. (2021) · Research Square · doi:10.21203/rs.3.rs-969651/v1 · Cited by 3
Development of Textual Analysis using Machine Learning to Improve the Sentiment Classification
Rajpoot, A. K., Nand, P., & Abidi, A. I. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2062/1/012014 · Cited by 2
“Bi-LSTM + CNN [14].”
BiGRU-Attention Sentiment Analysis for Enhancing Task-Oriented Chatbots
Trang, N. T. M., & Hung, N. N. (2024) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi61536.2024.00075 · Cited by 3
MultiSHTM: Multi-Level Attention Enabled Bi-Directional Model for the Summarization of Chart Images
Kumari, I., & Lee, H. (2025) · IEEE Access · doi:10.1109/access.2025.3568849 · Cited by 2
“The input gate acquires the input which maintains the flow of information from the current cell to the upgraded cell state [28].”
Islam, M. Z., Rafi, M. F. A., Podder, P., Siddika, A., Kabir, M., Khan, E. M., Islam, A. K. M. N., & Mukta, S. H. (2025) · Data and Information Management · doi:10.1016/j.dim.2025.100104 · Cited by 2
Li, H., & Li, Z. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425407648 · Cited by 2
Zhao, J., & Chen, X. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425407697 · Cited by 2
FutureGS: Structured Gaussian Fields for Future-Aware Dynamic Scene Modeling
Ding, M., Wang, Z., Wang, J., Han, T., Hu, X., Ding, J., Tan, M., & Kuang, Z. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755428 · Cited by 2
Amin, I., Fakhr, I., Fakhr, M. W., & Kashef, R. F. (2025) · IEEE Access · doi:10.1109/access.2025.3627187 · Cited by 2
KİTLE FONLAMASINDAKİ PROJE METİN İÇERİKLERİNİN LSTM İLE ANALİZİ
Kılınç, M., Aydın, C., & Tarhan, Ç. (2022) · Journal of Research in Business · doi:10.54452/jrb.1021694 · Cited by 2
illustration of attention mechanism in LSTM algorithm for analyzing its performance
Bhuvaneswari, M., & Praba, V. L. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns2.7335 · Cited by 2
Evaluation of the Development Value of Emotional Data Mining in Mass Media Using the RBTM Model
Yang, W. (2023) · IEEE Access · doi:10.1109/access.2023.3304677 · Cited by 2
“[9] used recursive neural networks and recursive tensor neural network models to process text sentiment analysis without needing any pre-defined sentiment lexicon or polarity conversion rule.”
MethodExploring the Hybrid Neural Network and Attention Mechanism for Classification of Social Bias
Hamzah, S., Mohd, M., & Zakaria, L. Q. (2023) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse59128.2023.10298845 · Cited by 2
“Such related works by [13] whose proposed Bi-LSTM+CNN hybrid model on IMDB movie review dataset The model employ single convolutional layer from word2vec embedding 500 dimension, bidirectional LSTM and single attention layer to obtain the future context and historical context.”
WE-BTR: A Behavior Tree Recommendation Method Based on Word Embedding
Su, H., Fu, L., Wang, X., Li, J., Wu, Y., & Wang, Y. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00146 · Cited by 2
“This technique facilitates various tasks in natural language processing that draw on vector space models, including text classification [21], sentiment analysis [22], and language translation [23].”
MethodDeep Learning Approach for Aspect-Based Sentiment Analysis on Indonesian Hospitals Reviews
Fahmi, M., Yudanto, F., Nazhifah, N., Sari, Y. A. P., & Afiahayati (2023) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic60109.2023.10381908 · Cited by 2
“In research [5] [6] [7], the researcher utilizes the aspect extraction method with POS Tagger to identify aspects and opinion words before classifying aspects and sentiments.”
MethodWubet, Y. A., & Lian, K. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100227 · Cited by 4
Wu, Y., Lu, T., Liu, C., Guo, F., & Luo, X. (2023) · International Conference on Computer Science and Automation Technology (CSAT) · doi:10.1109/csat61646.2023.00139 · Cited by 2
Enhancing Multi-Class Text Classification with BERT-Based Models
Wu, H., Ye, X., & Manoharan, S. (2023) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde59766.2023.10487760 · Cited by 2
“Jang et al. [6] contend that a mere combination of CNN and LSTM might not be the optimal solution due to the substantial number of features requiring consideration for classification.”
BackgroundEvaluation of Different Machine Learning Methods for Sentiment Analysis of Indian Languages
Sharma, D., & Shilpa, S. (2024) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito61523.2024.10522429 · Cited by 2
Machine Learning Methods for Extracting Newspaper Articles from PDF Files
Fatima, P., Fathima, S., Khatatneh, A. M. A., & Qudah, M. K. A. (2024) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit61487.2024.10580612 · Cited by 2
Metu, M. A., Akhter, N., Nasrin, S., Anzum, T., Khatun, A., & Mazumder, R. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.8.914-922 · Cited by 2
“Jang et al. [13] scrutinized the limitations of Long Short-Term Memory (LSTM) networks, revealing challenges in achieving optimal precision, F1-Score, accuracy, and recall.”
BackgroundAnoop, K. R., Deepak, P., Gangan, M. P., Abraham, S. S., & Lajish, V. L. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02194-4 · Cited by 2
Vasconcelos, L. L., & Campelo, C. E. C. (2024) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2024.3759 · Cited by 2
COVID-19 Tweets Analysis Using Hybrid Bi-LSTM and Transformer Models
Bader, M., Shahin, I. M. A., & Ahmed, A. H. (2024) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset60340.2024.10708752 · Cited by 2
Comparative Study of BERT-CNN, TRANS-BLSTM, and RoBERTa Models for Sentiment Analysis
Silitonga, C. A. A., Dermawan, M. D., Adeta, F., & Nadia, N. (2024) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee63424.2024.10730177 · Cited by 2
Research on Text Classification Based on LSTM-CNN
Zhang, H. Q. (2024) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3708036.3708084 · Cited by 2
Rani, G., Misra, A., Kundu, N., Dhaka, V. S., & Lather, P. (2025) · IEEE Access · doi:10.1109/access.2025.3558394 · Cited by 2
“In synergy with the above, the authors in [12] used CNN-Bidirectional LSTM architectures for efficiently extracting the context of succeeding and preceding tokens.”
ERNIE-TextCNN: research on classification methods of Chinese news headlines in different situations
Yan, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-14955-4 · Cited by 2
“…research on text classification turned to deep neural network models, such as Recurrent Neural Network (RNN) 20 , Convolutional Neural Network (CNN) 21 , Long Short-Term Memory Neural Network (LSTM) 22 , FastText 23 , etc. Guan Pengfei et al. 24 used a bidirectional LSTM structure to learn the…”
Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classification
Katamesh, N., Abu-Elnasr, O., & Elmougy, S. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015761 · Cited by 3
“Moreover, machine learning algorithms, such as Deep Neural Network (DNN) [4,5], Recurrent Neural Network (RNN) [4,5], Convolutional Neural Network (CNN) [4,5], Recurrent CNN (RCNN) [6,7], Long short-Term Memory (LSMT) model [4,8] and Bidirectional LSTM (BiLSTM) [9,10], are used to train the document classi cation mode…”
MethodA Framework for False Negative Detection in NER/NEL
Quijada, M., Vivó, M., Abella-Bascarán, Á., Chocrón, P., & de Maeztu, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08473-7_30 · Cited by 2
Overview of the Benefits Deep Learning Can Provide Against Fake News, Cyberbullying and Hate Speech
Al-Dala’in, T., & Zhao, J. H. S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35308-6_2 · Cited by 2
Xiong, Y., & Dong, S. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10182514 · Cited by 2
“Another advantage is that they can improve the interpretability of the model by identifying which parts of the input sequence are most important for the output.[9].”
BackgroundSocial Spider Optimization for Text Classification Enhancement
Al–Anzi, F. S., & Sarath, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-39774-5_59 · Cited by 2
Sridharan, T. B., & Akilashri, P. S. S. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01147-z · Cited by 2
“The Random Forest (RF) (Paul et al. 2018), Bi-LSTM (Jang et al. 2020), TCN (Rao and Liu (43”
MFF-SC: A multi-feature fusion method for smart contract classification
Tian, G., Wang, X., Wang, R., Yu, Q., & Zhao, G. (2023) · Intelligent Data Analysis · doi:10.3233/ida-227186 · Cited by 2
Comparative Analysis of Deep Learning Models for Telugu News Text Classification
Settineni, P., Veerendra, B., Reddy, M. S. J., Samuel, P., & Sumathi, D. (2023) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit59427.2023.10430996 · Cited by 2
Boosting Text Classification Performance for Unlabeled Data with Semi-Supervised Learning
Khondokar, T., Islam, S., Chadni, T. I. A., & Khandokar, I. A. (2023) · IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) · doi:10.1109/wiecon-ece60392.2023.10456460 · Cited by 2
“In this paper [8], It is trained on the Internet Movie Database (IMDB) movie review dataset.”
MethodSun, Y., Keung, J., Yang, Z., Yu, H. K., Luo, W., & Liao, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5044241 · Cited by 2
Applications of Natural Language Processing for Industry 4.0 Skills Development
Caratozzolo, P., Alvarez-Delgado, A., & Rodríguez‐Ruiz, J. (2023) · Future of Educational Innovation-Workshop Series Data in Action · doi:10.1109/ieeeconf56852.2023.10104796 · Cited by 1
“Third, NLP tools were used to determine the quality of the texts (using Latent Dirichlet's Analysis) and check the level of achievement of the students (using Long-Short Term Memory Networks) [34], [35].”
MethodLong Text Classification Using Pre-trained Language Model for a Low-Resource Language
Yohannes, H. M., & Amagasa, T. (2023) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict58900.2023.00026 · Cited by 1
Deep neural net based approach for air pressure prediction
Karmakar, S., Roy, S., Kar, A., Basak, M., Ghosh, T., & Biswas, S. (2023) · AIP conference proceedings · doi:10.1063/5.0166718 · Cited by 1
Ripple20 Vulnerabilities Detection using a Featureless Deep Learning Model
Hulayyil, S. B., & Li, S. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00168 · Cited by 1
“These are then passed into CNN layers, which use two types of layers to learn from the instances of higher-level features [17].”
BackgroundInformation Retrieval and Recommendation Framework Based on ChatGPT
Yang, W., & Zhang, S. (2024) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl61749.2024.00068 · Cited by 1
Çelikten, T., & Onan, A. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.051574 · Cited by 2
PWAM-LSTM: prime word focused attention mechanism in long short term memory
Sowmyayani, S., & Murugan, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19986-z · Cited by 1
“Another method has combined BiLSTM and CNN model with attention mechanism [14] that capitalizes on the advantages of LSTM and CNN with an additional attention mechanism and tested it using IMDB Movie review dataset.”
Regardless of Sensor Location Human Activity Recognition Based on Transformer
He, Z., Tian, Y., He, Z., & Sun, Y. (2024) · International Conference on Cyber Security and Information Engineering (ICCSIE) · doi:10.1145/3689236.3695396 · Cited by 1
“Similar to recurrent neural networks, the Transformer efficiently processes time series classification by leveraging correlations between features within a time step, outperforming recurrent on stacked attention mechanisms demonstrated effectiveness in addressing complex time series problems, such as NLP[15].”
Karn, A., & Sah, A. K. (2024) · Journal of Kathmandu BernHardt College · doi:10.3126/jkbc.v6i1.72962 · Cited by 1
Wang, X. R., Suo, W., & Wang, R. (2025) · Algorithms · doi:10.3390/a18020067 · Cited by 1
Graph affine Transformer with a symmetric adaptation strategy for text classification
Ma, M., Gong, H., & Ding, Y. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06992-7 · Cited by 1
“To combine the strengths of CNNs and RNNs, some studies proposed the use of a CNN-LSTM hybrid model for text feature extraction [36–38].”
Unenhanced Sparse Vector-based Embedding Method for Sentiment Analysis
Kishore, G. R., Harish, B. S., & Roopa, C. K. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.10098 · Cited by 1
Multiclass Emoji Prediction: Integrating BERT and CNN-BiLSTM for Improved Performance
Sen, S., Islam, M. S., & Sultana, Z. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022597 · Cited by 1
“Despite this method significantly improving multi-class prediction, its core objective was to increase the number of classification labels, and it had imperfections for larger applications [3].”
Ullah, S., Uddin, M. I., Adnan, M., Alarood, A. A., Alsulami, A. A., & Habibullah, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0324847 · Cited by 1
Kunwar, F. B., Ramachandran, A., Manigandan, E., Srivastava, S., Priya, S., & Suhane, S. (2025) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss63372.2025.11076538 · Cited by 1
“A Bi-LSTM+CNN hybrid model [15] incorporating an attention mechanism enhances text classification accuracy on IMDB data by utilising contextual information.”
Stefanovič, P., Radvilaitė, U., Pliuskuvienė, B., & Ramanauskaitė, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-11877-z · Cited by 1
Dual-Path Short Text Classification with Data Optimization
Li, W., Lv, G., & He, Y. (2025) · Applied Sciences · doi:10.3390/app152011015 · Cited by 1
Musham, N. K., Musam, V. S., Hasan, S. B., Bharathidasan, S., Spandana, S. G., & Abu-Adaiq, H. (2025) · Journal of Multiscale Modelling · doi:10.1142/s1756973726400056 · Cited by 1
Alemu, A. A., Melese, M. D., Mengesha, D. A., & Widneh, M. A. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-08121-8 · Cited by 1
Integrating clustering with evolutionary feature selection using ENORA and SToWVector
Mackenzie-Rivero, A. J., Martínez‐Béjar, R., & Meléndez, H. V. (2025) · Array · doi:10.1016/j.array.2025.100508 · Cited by 1
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
Al-Bayati, J. S. H., Tawfeeq, F. N., & Al-Shammaa, M. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.11.1604-1623 · Cited by 1
Saha, S., Mukhopadhyay, P., Mahata, K., Basu, S., & Majumder, K. (2026) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-026-00233-3 · Cited by 1
Calvo, M. A. S., & Lee, H. S. (2026) · Applied Ocean Research · doi:10.1016/j.apor.2026.105131 · Cited by 1
Metadata
- Authors
- Jang, B., Kim, M., Harerimana, G., Kang, S.-U., & Kim, J. W.
- Affiliations
- Sangmyung University (KR)
- Corresponding author
- Kim, Jong Wook
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-08-24
- Volume
- 10
- Issue
- 17
- Pages
- 5841
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3080359515
- Cited by
- 442
- References
- 33
- Citations by year
- 2026: 25 · 2025: 93 · 2024: 97 · 2023: 101 · 2022: 73 · 2021: 50 · 2020: 3
- FWCI
- 22.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (NRF-2019R1F1A1058058)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- text classification, bidirectional LSTM, convolutional neural networks, attention mechanism, Word2Vec, IMDB movie reviews, sentiment analysis, feature extraction, long short-term memory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
