Unsupervised model for aspect categorization and implicit aspect extraction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-022-01678-5.pdf
Reference59 articles.
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2. Liu W, Jing W, Li Y (2020) Incorporating feature representation into BiLSTM for deceptive review detection. Computing 102:701–715. https://doi.org/10.1007/s00607-019-00763-y
3. Liu B (2017) Many facets of sentiment analysis. In: A practical guide to sentiment analysis. Socio-affective computing. Springer, Cham, pp 11–39. https://doi.org/10.1007/978-3-319-55394-8_2
4. Poria S, Chaturvedi I, Cambria E, Bisio F (2016b) Sentic LDA: improving on LDA with semantic similarity for aspect-based sentiment analysis. In: International joint conference on neural networks. IEEE, pp 4465–4473. https://doi.org/10.1109/IJCNN.2016.7727784
5. Xu X, Cheng X, Tan S et al (2013) Aspect-level opinion mining of online customer reviews. China Commun 10:25–41. https://doi.org/10.1109/CC.2013.6488828
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1. Deep Learning Using Context Vectors to Identify Implicit Aspects;IEEE Access;2023
2. An improved Bi-LSTM performance using Dt-WE for implicit aspect extraction;2022 International Conference on Data Science and Intelligent Computing (ICDSIC);2022-11-01
3. Joint aspect-opinion extraction and sentiment orientation detection in university reviews;International Journal of Information Technology;2022-07-31
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