The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A CNN-BiLSTM Model for Document-Level Sentiment Analysis
Open accessAbstract
Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press articles and blog posts about a particular product or company, and it requires a high concentration, especially when the topic being discussed is sensitive. Nevertheless, most existing models and techniques are designed to process short text from social networks and collaborative platforms. In this paper, we propose a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models, with Doc2vec embedding, suitable for opinion analysis in long texts. The CNN-BiLSTM model is compared with CNN, LSTM, BiLSTM and CNN-LSTM models with Word2vec/Doc2vec embeddings. The Doc2vec with CNN-BiLSTM model was applied on French newspapers articles and outperformed the other models with 90.66% accuracy.
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Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Bidirectional recurrent neural networks
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Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
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Improving neural networks by preventing co-adaptation of feature detectors
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Attention-based LSTM for Aspect-level Sentiment Classification
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Cited by
A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
Tan, K. L., Lee, C. P., & Lim, K. M. (2023) · Applied Sciences · doi:10.3390/app13074550 · Cited by 303
Pimpalkar, A., & Raj R, J. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117581 · Cited by 98
Metadata
- Authors
- Rhanoui, M., Mikram, M., Yousfi, S., & Barzali, S.
- Venue
- Machine Learning and Knowledge Extraction
- Publisher
- MDPI AG
- Published
- 2019-07-25
- Volume
- 1
- Issue
- 3
- Pages
- 832-847
- ISSN
- 2504-4990 (ISSN-L)
- OpenAlex
- W2962933419
- Cited by
- 300
- References
- 9
- FWCI
- 10.48
- Type
- journal article
- Language
- en
- Open access
- gold
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