The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification
Open accessAbstract
Duyu Tang, Bing Qin, Ting Liu. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.
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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
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Convolutional 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
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014) · Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.3115/v1/p14-5010 · Cited by 7,037
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,720
Distributed Representations of Sentences and Documents
Le, Q. V., & Mikolov, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.4053 · Cited by 4,934
Cited by
A review on sentiment analysis and emotion detection from text
Nandwani, P., & Verma, R. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00776-6 · Cited by 869
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841
Aligning AI With Shared Human Values
Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., & Steinhardt, J. (2020) · arXiv · doi:10.48550/arxiv.2008.02275 · Cited by 517
A survey on sentiment analysis and opinion mining for social multimedia
Li, Z., Fan, Y., Jiang, B., Lei, T., & Liu, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6445-z · Cited by 164
Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor
Kazienko, P., Bielaniewicz, J., Gruza, M., Kanclerz, K., Karanowski, K., Miłkowski, P., & Kocoń, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.010 · Cited by 38
Metadata
- Authors
- Tang, D., Qin, B., & Liu, T.
- Venue
- Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2015
- Pages
- 1014-1023
- OpenAlex
- W2251292973
- Cited by
- 365
- References
- 44
- FWCI
- 25.57
- Type
- conference paper
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: Learning Semantic Representations of Users and Products for Document Level Sentiment Classification. https://app.cochat.ai/papers/10.3115/v1/p15-1098
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- Tang, D., Qin, B., & Liu, T. (2015). Learning Semantic Representations of Users and Products for Document Level Sentiment Classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). https://doi.org/10.3115/v1/p15-1098
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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.
