Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

Jang, Beakcheol; Kim, Myeonghwi; Harerimana, Gaspard; Kang, Sang-ug; Kim, Jong Wook

doi:10.3390/app10175841

Cited by 442Topic ModelingSentiment Analysis and Opinion MiningText and Document Classification Technologies

Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

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Jang, Beakcheol, Kim, Myeonghwi, Harerimana, Gaspard, Kang, Sang-ug, Kim, Jong Wook·2020·Applied Sciences

DOI 10.3390/app10175841

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Abstract

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.

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LSTM models can capture long-term dependencies between word sequences, which is a vital requirement in the context of cyberbullying detection.
“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]
Section 3 (p. 6) says LSTM gating mechanisms help track long-term dependencies. The paper discusses sentiment and text classification, but does not establish that this capability is a vital requirement for cyberbullying detection. (LLM, conf=0.86)
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Alshehri, F., & Rahman, A. (2023) · Water · doi:10.3390/w15122298 · Cited by 69 · see citing works
Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data
Zhang, Z., Zhang, S., Ni, D., Wei, Z., Yang, K., Jin, S., Huang, G., Liang, Z., Zhang, L., Li, L., et al. (2024) · Sensors · doi:10.3390/s24123714 · Cited by 68 · see citing works
“The BiLSTM model extracts bidirectional semantic information from text data while preserving long-term relationships between words and repeated contextual information [37].”
Method
A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision
Nafea, A. A., Alameri, S. A., Majeed, R. R., Khalaf, M. A., & AL-Ani, M. M. (2024) · Babylonian Journal of Machine Learning · doi:10.58496/bjml/2024/004 · Cited by 67 · see citing works
“These hybrid methods aim to enhance performance and address the limitations of individual methods like combination DL models with usual classifiers, utilized DL for feature extraction followed by supervised ML for classification, and combining supervised learning into deep architecture [55], [56].”
Method
Techniques of Sarcasm Detection: A Review
Verma, P., Shukla, N., & Shukla, A. P. (2021) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite51222.2021.9404585 · Cited by 64 · see citing works
A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi – LSTM model for semantic text similarity identification
Viji, D., & Revathy, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11771-6 · Cited by 63 · see citing works
“The researchers ought to pass out single-sentences by using BERT framework, to derive fixed-size embedded vector through averaging the values of outputs [12].”
Method
Effective text classification using BERT, MTM LSTM, and DT
Jamshidi, S., Mohammadi, M., Bagheri, S., Najafabadi, H. E., Rezvanian, A., Gheisari, M., Ghaderzadeh, M., Shahabi, A. S., & Wu, Z. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102306 · Cited by 64 · see citing works
Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization
Jiang, J., Zhang, H., Dai, C., Zhao, Q., Feng, H., Ji, Z., & Ганчев, И. (2021) · IEEE Access · doi:10.1109/access.2021.3110143 · Cited by 59 · see citing works
“In June 2017, the Google team proposed that a complete end-to-end processing could be realized without using a CNN or a RNN, but only using the encoder/decoder attention in a self-attention manner [25].”
MethodInfluential
A BERT-Based Hybrid Short Text Classification Model Incorporating CNN and Attention-Based BiGRU
Bao, T., Ren, N., Luo, R., Wang, B., Gengyu, S., & Guo, T. (2021) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.294580 · Cited by 57 · see citing works
Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning
Sweidan, A. H., El-Bendary, N., & Al-Feel, H. (2021) · IEEE Access · doi:10.1109/access.2021.3091394 · Cited by 57 · see citing works
A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service
Li, Q., Li, X., Lee, B., & Kim, J. (2021) · Applied Sciences · doi:10.3390/app11188613 · Cited by 56 · see citing works
“The BiLSTM is utilized for encoding long-distance word dependencies effectively [52,53].”
BackgroundMethodSelf-citation
A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset
Abayomi‐Alli, O., Misra, S., & Abayomi-Alli, A. A. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6989 · Cited by 53 · see citing works
“The unique feature of RNN architecture is the application of a gating mechanism consisting of an input gate, forget gate, and output gate, 16 which aids in addressing the preservation of long-term information.”
Background
The Impact of Translating Resource-Rich Datasets to Low-Resource Languages Through Multi-Lingual Text Processing
Ghafoor, A., Imran, A. S., Daudpota, S. M., Kastrati, Z., Abdullah, A., Batra, R., & Wani, M. A. (2021) · IEEE Access · doi:10.1109/access.2021.3110285 · Cited by 52 · see citing works
Evolving techniques in sentiment analysis: a comprehensive review
Kumar, M., Khan, L., & Chang, H.-T. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2592 · Cited by 75 · see citing works
An intelligent music genre analysis using feature extraction and classification using deep learning techniques
Hongdan, W., SalmiJamali, S., Chen, Z., Qiaojuan, S., & Ren, L. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107978 · Cited by 47 · see citing works
Attention-Based 1D CNN-BiLSTM Hybrid Model Enhanced with FastText Word Embedding for Korean Voice Phishing Detection
Boussougou, M. K. M., & Park, D.-J. (2023) · Mathematics · doi:10.3390/math11143217 · Cited by 43 · see citing works
A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors
Barrera, K., Burriel-Valencia, J., Sapena-Bañó, Á., & Martínez‐Román, J. (2023) · Sensors · doi:10.3390/s23198196 · Cited by 43 · see citing works
“Accuracy [54] is a statistical measure commonly employed in binary or multiclass classiﬁcations and is applicable in this context.”
Background
Advanced Intrusion Detection in MANETs: A Survey of Machine Learning and Optimization Techniques for Mitigating Black/Gray Hole Attacks
Hassan, S. M., Mohamad, M. M., & Muchtar, F. (2024) · IEEE Access · doi:10.1109/access.2024.3457682 · Cited by 43 · see citing works
“This unique structure enables the network to simultaneously harness past and future context information at each point in the sequence [75].”
Background
A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges
Arif, M. (2021) · Journal of Information Security and Cybercrimes Research · doi:10.26735/gbtv9013 · Cited by 45 · see citing works
“…neural networks [113], Deep belief networks [115], Deep recurrent neural networks [113], Deep stacking networks [117], Generative Adversarial networks [118], LSTM [117] and variants of LSTM [119, 120], etc. Convolutional neural networks were originally designed for image processing [121].”
Background
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
Das, R., Ahmed, W., Sharma, K., Hardey, M., Dwivedi, Y. K., Zhang, Z., Apostolidis, C., & Filieri, R. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.03.008 · Cited by 49 · see citing works
Combination of GRU and CNN Deep Learning Models for Sentiment Analysis on French Customer Reviews Using XLNet Model
Habbat, N., Anoun, H., & Hassouni, L. (2022) · IEEE Engineering Management Review · doi:10.1109/emr.2022.3208818 · Cited by 40 · see citing works
“14%, Similarly, in [35] and [36], the best result using the combination of BiLSTM and CNN with Word2vec was achieved.”
Method
Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)
Jena, R., Naik, S. P., Pradhan, B., Beydoun, G., Park, H., & Alamri, A. M. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2021.102642 · Cited by 39 · see citing works
An Effective Hybrid Deep Neural Network for Arabic Fake News Detection
Wotaifi, T. A., & Dhannoon, B. N. (2023) · Baghdad Science Journal · doi:10.21123/bsj.2023.7427 · Cited by 38 · see citing works
“Convolution Neural Network (CNN) is a class of deep learning and feed-forward artificial neural networks where connections between nodes do not form a cycle 13 .”
Background
Enhancing Multilingual Hate Speech Detection: From Language-Specific Insights to Cross-Linguistic Integration
Hashmi, E., Yayilgan, S. Y., Hameed, I. A., Yamin, M. M., Ullah, M., & Abomhara, M. (2024) · IEEE Access · doi:10.1109/access.2024.3452987 · Cited by 42 · see citing works
“In our study, we employed Long Short-Term Memory (LSTM) [63] and its variant, Bidirectional LSTM (BiLSTM) [64], [65].”
Method
A fine-grained and multi-context-aware learning path recommendation model over knowledge graphs for online learning communities
Zhang, S., Hui, N., Zhai, P., Xu, J., Cao, L., & Wang, Q. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103464 · Cited by 41 · see citing works
SceneFND: Multimodal fake news detection by modelling scene context information
Zhang, G., Giachanou, A., & Rosso, P. (2022) · Journal of Information Science · doi:10.1177/01655515221087683 · Cited by 34 · see citing works
“Bi-LSTM Bi-LSTM has been proven to be effective in many text classification tasks [32].”
Method
Prediction of crop yield in India using machine learning and hybrid deep learning models
Saravanan, K. S., & Bhagavathiappan, V. (2024) · Acta Geophysica · doi:10.1007/s11600-024-01312-8 · Cited by 32 · see citing works
“The CNN used for extracting the features (Jang et al. 2020) of crop yield dataset consists of four convolutional layers with activation function, four batch normalization layer each followed by max pooling layer and finally a fully connected layer.”
Utilizing social media and machine learning for personality and emotion recognition using PERS
Talaat, F. M., El-Gendy, E. M., Saafan, M. M., & Gamel, S. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08962-7 · Cited by 32 · see citing works
Emotion Detection in Textual Data using Deep Learning
Yohanes, D., Putra, J. S., Filbert, K., Suryaningrum, K. M., & Saputri, H. A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.547 · Cited by 31 · see citing works
Sentiment analysis applications using deep learning advancements in social networks: A systematic review
Ramezani, E. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129862 · Cited by 35 · see citing works
A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model
Habbat, N., Anoun, H., & Hassouni, L. (2021) · International Journal on Electrical Engineering and Informatics · doi:10.15676/ijeei.2021.13.4.3 · Cited by 28 · see citing works
Automated Amharic News Categorization Using Deep Learning Models
Endalie, D., & Haile, G. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/3774607 · Cited by 27 · see citing works
“In [14], the authors proposed an attention-based bidirectional long short-term memory (Bi-LSTM) +CNN hybrid model text classification.”
Background
Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model
Kumar, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-05974-6 · Cited by 27 · see citing works
PREIUD: An Industrial Control Protocols Reverse Engineering Tool Based on Unsupervised Learning and Deep Neural Network Methods
Ning, B., Zong, X., He, K., & Lian, L. (2023) · Symmetry · doi:10.3390/sym15030706 · Cited by 27 · see citing works
Research on Log Anomaly Detection Based on Sentence-BERT
Hu, C., Xuekui, S., Dai, H., Zhang, H., & Liu, H. (2023) · Electronics · doi:10.3390/electronics12173580 · Cited by 27 · see citing works
Text Classification Model Based on Multi-head self-attention mechanism and BiGRU
Fang, F., Hu, X., Shu, J., Wang, P., Shen, T., & Li, F. (2021) · IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) · doi:10.1109/tocs53301.2021.9688981 · Cited by 25 · see citing works
SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation
Thiengburanathum, P., & Charoenkwan, P. (2022) · IEEE Access · doi:10.1109/access.2023.3308951 · Cited by 30 · see citing works
“Recently in SA, DL techniques that operate in conjunction with the convolution CNN, LSTM, BiLSTM, and GRU have been investigated to produce an even more accurate model, as can be seen from the work [24], [25], [26].”
Method
Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism
Yafooz, W. M. S. (2024) · Information · doi:10.3390/info15060316 · Cited by 25 · see citing works
Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering
Khekare, G. S., Kumar, K. P., Prasanthi, K. N., Godla, S. R., Rachapudi, V., Ansari, M. S. A., & El–Ebiary, Y. A. B. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141262 · Cited by 24 · see citing works
“Lastly, as mentioned in, the F1-score represents the harmonious average of recall and precision Top of Form [19].”
Background
Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis
Capuano, N., Caballé, S., Conesa, J., & Greco, A. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02747-9 · Cited by 24 · see citing works
“The main deep learning models used for text classification are largely based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) (Jang et al. 2020).”
A Hybrid Approach for Automated Short Answer Grading
Kaya, M. A., & Cicekli, I. (2024) · IEEE Access · doi:10.1109/access.2024.3420890 · Cited by 23 · see citing works
“They have demonstrated an advantage over standard CNNs and LSTMs when combined with LSTMs [26], [27].”
Background
eRAD-Fe: Emotion Recognition-Assisted Deep Learning Framework
Kim, S., Nguyen, N. A. T., Yang, H.-J., & Lee, S. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3115195 · Cited by 23 · see citing works
EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records
Wang, G., Lou, X., Guo, F., Kwok, D., & Cao, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3346210 · Cited by 23 · see citing works
“Among the ﬁve chosen representative meth-ods, the CNN method [40], long short-term memory (LSTM) method [41] and bidirectional LSTM (Bi-LSTM) method [42] are selected as traditional deep learning methods.”
Method
RoBERTa, ResNeXt and BiLSTM with self-attention: The ultimate trio for customer sentiment analysis
Lak, A. J., Boostani, R., Alenizi, F. A., Mohammed, A. S., & Fakhrahmad, S. M. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112018 · Cited by 22 · see citing works
An Unsupervised Neural Network Feature Selection and 1D Convolution Neural Network Classification for Screening of Parkinsonism
Mian, T. S. (2022) · Diagnostics · doi:10.3390/diagnostics12081796 · Cited by 22 · see citing works
“The common applications of CNN in computer vision tasks are [16] image classification [17], object detection [18], image segmentation [19], medical domain [20], agriculture domain [21], scene understanding [22], and text classification.”
Background
E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning
Petroșanu, D.-M., Pîrjan, A., Căruțașu, G., Tăbușcă, A., Zirra, D., & Perju-Mitran, A. (2022) · Electronics · doi:10.3390/electronics11182940 · Cited by 28 · see citing works
“…have implemented in their studies the BiLSTMs in developing a wide range of applications such as: healthcare [67,68], energy forecasting [69,70], natural gas demand forecasting [71], human activity recognition [72], text classiﬁcation [73], image classiﬁcation [74], trafﬁc accident detection [75].”
A Convolutional Neural Network Algorithm for Pest Detection Using GoogleNet
Yulita, I. N., Rambe, M. F. R., Sholahuddin, A., & Prabuwono, A. S. (2023) · AgriEngineering · doi:10.3390/agriengineering5040145 · Cited by 22 · see citing works
An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector
Kalidoss, L., Thouti, S., Arunachalam, R., & Ramamurthy, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125275 · Cited by 22 · see citing works
Contractor’s Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology
Choi, S.-W., & Lee, E. (2022) · Sustainability · doi:10.3390/su14116938 · Cited by 21 · see citing works
Exploiting Stacked Autoencoders for Improved Sentiment Analysis
Ahmed, K., Nadeem, M. I., Li, D., Zheng, Z., Ghadi, Y. Y., Assam, M., & Mohamed, H. G. (2022) · Applied Sciences · doi:10.3390/app122312380 · Cited by 21 · see citing works
Sustainable residential building energy consumption forecasting for smart cities using optimal weighted voting ensemble learning
Alymani, M., Mengash, H. A., Aljebreen, M., Alasmari, N., Allafi, R., Alshahrani, H., Elfaki, M. A., Hamza, M. A., & Abdelmageed, A. A. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103271 · Cited by 21 · see citing works
Detecting command injection attacks in web applications based on novel deep learning methods
Wang, X., Zhai, J., & Yang, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-74350-3 · Cited by 20 · see citing works
“For embedding, we apply two distinct methods: the word vector model from Jang et al. 23 for the word set W , and a character mapping-based symbol vector for the symbol set C .”
Method
Character gated recurrent neural networks for Arabic sentiment analysis
Omara, E., Mousa, M., & Ismail, N. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-13153-w · Cited by 20 · see citing works
Developing a fake news identification model with advanced deep languagetransformers for Turkish COVID-19 misinformation data
Bozuyla, M., & Özçift, A. (2022) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.3818 · Cited by 20 · see citing works
Hybrid approach of deep feature extraction using BERT– OPCNN & FIAC with customized Bi-LSTM for rumor text classification
Nithya, K., Murugasamy, K., Easwaramoorthy, S. V., Dhivyaa, C. R., Yoo, S., & Cho, J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.01.056 · Cited by 20 · see citing works
Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews
Cahyaningtyas, S., Fudholi, D. H., & Hidayatullah, A. F. (2021) · Kinetik Game Technology Information System Computer Network Computing Electronics and Control · doi:10.22219/kinetik.v6i3.1300 · Cited by 20 · see citing works
Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable Approach
Gambo, I., Massenon, R., Lin, C., Ogundokun, R. O., Agarwal, S., & Pak, W. (2024) · IEEE Access · doi:10.1109/access.2024.3443527 · Cited by 19 · see citing works
“However, Bidirectional LSTM (BiLSTM) networks have proven very effective for text classification and sequence labeling tasks across various domains [86], [87].”
Background
Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm
Ma’aly, A. N., Pramesti, D., Fathurahman, A. D., & Fakhrurroja, H. (2024) · Information · doi:10.3390/info15110705 · Cited by 19 · see citing works
Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China
Wang, Q., Liu, Y., Yue, Q., Zheng, Y., Yao, X., & Yu, J. (2020) · Water · doi:10.3390/w12123532 · Cited by 19 · see citing works
Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
Chen, S., Xi, J., Chen, Y., & Zhao, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4851615 · Cited by 19 · see citing works
“Text classiﬁcation is a common task of NLP, which concerns training mathematical models to gain a certain generalization ability by inputting a group of texts with relevant classiﬁcation labels so that the model can better predict the categories of other texts in the same ﬁeld [18].”
Background
Task Recommendation Method Combining Multimodal Cognition and Collaboration in Mobile Crowdsensing Systems
Wang, J., Yan, Y., & Zhao, G. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109796 · Cited by 19 · see citing works
Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
Zhu, X., Zou, F. C., & Li, S. (2024) · Applied Sciences · doi:10.3390/app14135787 · Cited by 19 · see citing works
Offensive language identification in Dravidian code mixed social media text
Saumya, S., Kumar, A., & Singh, J. P. (2021) · Cited by 19 · see citing works
Classification of User Comment Using Word2vec and Deep Learning
Kurnia, R. I., & Girsang, A. S. (2021) · International Journal of Emerging Technology and Advanced Engineering · doi:10.46338/ijetae0521_01 · Cited by 19 · see citing works
Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model
Mahto, D., Yadav, S. C., & Lalotra, G. S. (2022) · Mobile Information Systems · doi:10.1155/2022/1068554 · Cited by 19 · see citing works
A deep dive into delhi's air pollution: forecasting $${\varvec{P}}{{\varvec{M}}}_{2.5}$$ levels using a Bi-LSTM-GRU hybrid model
Ranjan, S., & Singh, S. K. (2025) · Earth Science Informatics · doi:10.1007/s12145-024-01627-6 · Cited by 18 · see citing works
“Bidirectional Long Short-Term Memory (Bi-LSTM) networks enhance traditional LSTM models by processing data in both forward and backward directions (Jang et al. 2020).”
Spatio-temporal graph Bert network for EEG emotion recognition
Yan, J., Du, C., Li, N., Zhou, X., Liu, Y., Wei, J., & Yang, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107576 · Cited by 18 · see citing works
Driving Range Estimation of Electric Vehicles using Deep Learning
George, D., & Sivraj, P. (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532912 · Cited by 18 · see citing works
“The two parallel layers in Bi-LSTM helps in forward and backward passes to gain information [32].”
Background
A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model
Wang, Y., Liu, P., Zhu, K., Liu, L., Zhang, Y., & Guang-li, X. (2022) · Applied Sciences · doi:10.3390/app122211366 · Cited by 18 · see citing works
Drought stress prediction and propagation using time series modeling on multimodal plant image sequences
Choudhury, S. D., Saha, S., Samal, A. K., Mazis, A., & Awada, T. N. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1003150 · Cited by 18 · see citing works
“Frontiers in Plant Science 09 in a single end-to-end model (Jang et al., 2020).”
Background
Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model
Mazraedoost, S., Malekroodi, H. S., Žuvela, P., Yi, M., & Liu, J. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00167 · Cited by 17 · see citing works
A Comprehensive Analysis of Transformer-Deep Neural Network Models in Twitter Disaster Detection
Balakrishnan, V., Shi, Z., Law, C. L., Lim, R., Teh, L. L., Fan, Y., & Periasamy, J. (2022) · Mathematics · doi:10.3390/math10244664 · Cited by 16 · see citing works
Detection of hateful twitter users with graph convolutional network model
Utku, A., Can, Ü., & Aslan, S. (2023) · Earth Science Informatics · doi:10.1007/s12145-023-00940-w · Cited by 16 · see citing works
“In doing so, it does not take into account unnecessary contextual information (Zhang et al. 2020; Jang et al. 2020).”
Background
Amharic political sentiment analysis using deep learning approaches
Alemayehu, F., Meshesha, M., & Abate, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-45137-9 · Cited by 16 · see citing works
Deep learning framework for congestion detection at public places via learning from synthetic data
Basalamah, S. M., Khan, S. D., Felemban, E. A., Naseer, A., & Rehman, F. U. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.11.005 · Cited by 15 · see citing works
“…that improves the performance of the model in various clas-siﬁcation tasks of sequential data, for example, classiﬁcation of transportation models (Liu and Lee, 2017), ECG signal classiﬁcation (Yildirim, 2018), text classiﬁcation (Jang et al., 2020), web services classiﬁcation (Ye et al., 2019).”
An Automated Text Document Classification Framework using BERT
Shah, M. A., Iqbal, M. J., Noreen, N., & Ahmed, I. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140332 · Cited by 15 · see citing works
“[8] employed MLP to classify textual data.”
Threshold-Learned CNN for Multi-Label Text Classification of Electronic Health Records
Yang, Z., & Emmert‐Streib, F. (2023) · IEEE Access · doi:10.1109/access.2023.3309157 · Cited by 16 · see citing works
“used word embedding method [19] to transform all words to unique vector representations.”
A Survey on Event Tracking in Social Media Data Streams
Han, Z., Shi, L., Liu, L., Jiang, L., Fang, J., Lin, F., Zhang, J., Panneerselvam, J., & Antonopoulos, N. (2023) · Big Data Mining and Analytics · doi:10.26599/bdma.2023.9020021 · Cited by 15 · see citing works

Sentiment Analysis Using an CNN-BiLSTM Deep Model Based on Attention Classification
Wang, Y. (2022) · Institutional Repositories DataBase (IRDB) · doi:10.15002/00025357
CoBiAt: A Sentiment ClassificatiCobiat: A Sentiment Classification Model Using Hybrid Convnet- Dual-lstm with Attention Mechanismon Model using Hybrid ConvNet- Dual-LSTM with Attention Mechanism
Ranjan, R., & Daniel, A. K. (2023) · Informatica · doi:10.31449/inf.v47i4.3911 · Cited by 3 · see citing works
Research on Text Classification Model based on the Fusion of Temporal and Feature Concerns
Li, L., & Chen, J. (2024) · International Conference on Compute and Data Analysis (ICCDA) · doi:10.1145/3705374.3705389
LSTM-CNN Hybrid Model for Text Classification
Zhang, J., Li, Y., Tian, J., & Li, T. (2018) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac.2018.8577620 · Cited by 135 · see citing works
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 · see citing works

Authors: Jang, B., Kim, M., Harerimana, G., Kang, S.-U., & Kim, J. W.
Affiliations: Sangmyung University (KR)
Corresponding author: Kim, Jong Wook
ORCID: Jang, Beakcheol
Kim, Myeonghwi
Harerimana, Gaspard
Kang, Sang-ug
Kim, Jong Wook
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2020-08-24
Volume: 10
Issue: 17
Pages: 5841
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app10175841
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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

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Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

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