The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis
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In the citing paper
“It creates a vector space representation of words based on their co-occurrence statistics within a large text corpus (Pimpalkar 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Pimpalkar 2022
Citations
Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
A Neural Probabilistic Language Model
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,479
A neural probabilistic language model
BengioYoshua, DucharmeRéjean, VincentPascal, & JanvinChristian (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944966 · Cited by 2,644
Proceedings of the International Joint Conference on Neural Networks, 2003.
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Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
Chen, T., Xu, R., He, Y., & Wang, X. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.065 · Cited by 730
Sentiment Analysis Based on Deep Learning: A Comparative Study
Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020) · Electronics · doi:10.3390/electronics9030483 · Cited by 593
Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks
Onan, A. (2021) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5909 · Cited by 396
Big Data: Deep Learning for financial sentiment analysis
Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018) · Journal of Big Data · doi:10.1186/s40537-017-0111-6 · Cited by 390
A CNN-BiLSTM Model for Document-Level Sentiment Analysis
Rhanoui, M., Mikram, M., Yousfi, S., & Barzali, S. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1030048 · Cited by 300
Research works printed but not linked: 3
- Hongmin Li, Xukun Li, Doina C, & Cornelia C. (2018). Comparison of Word Embeddings and Sentence Encodings as Generalized Representations for Crisis Tweet Classification Tasks, Proceedings of ISCRAM Asia Pacific.
- Nicholas, 2020, Working in detail: How LSTM hyperparameter selection influences sentiment analysis results, Archives of Data Science Series A, 6
- Zhang, 2017, A sensitivity analysis of (practitioners' guide to) convolutional neural networks for sentence classification, Proceeding International Joint Conference on Natural Language Processing, 1
Cited by
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Başarslan, M. S., & Kayaalp, F. (2023) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00386-3 · Cited by 99
Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer
Danyal, M. M., Khan, S. S., Khan, M., Ullah, S., Ghaffar, M. B., & Khan, W. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01250-9 · Cited by 59
Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning
Liyih, A., Anagaw, S., Yibeyin, M., & Tehone, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-63367-3 · Cited by 39
Ramezani, E. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129862 · Cited by 37
Metadata
- Authors
- Pimpalkar, A., & Raj R, J. R.
- Affiliations
- Sathyabama Institute of Science and Technology (IN)
- Corresponding author
- Pimpalkar, Amit
- ORCID
- Pimpalkar, Amit
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 203
- Pages
- 117581
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4280644435
- Cited by
- 98
- References
- 43
- Citations by year
- 2026: 14 · 2025: 30 · 2024: 34 · 2023: 15 · 2022: 5
- FWCI
- 6.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- sentiment analysis, social media, GloVe embeddings, CNN feature extraction, online reviews, short text classification, IMDB dataset
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis. https://app.cochat.ai/papers/10.1016/j.eswa.2022.117581
- The paper
- Pimpalkar, A., & Raj R, J. R. (2022). MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2022.117581
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
