![](bga.png)
and significantly outperforms other existing methods.
REFERENCES
Abu-Jbara, A., Ezra, J., and Radev, D. (2013). Purpose
and polarity of citation: Towards NLP-based biblio-
metrics. In Proceedings of the 2013 Conference of
the North American Chapter of the Association for
Computational Linguistics: Human Language Tech-
nologies, pages 596–606, Atlanta, Georgia. Associa-
tion for Computational Linguistics.
Athar, A. (2011). Sentiment analysis of citations using
sentence structure-based features. In Proceedings of
the ACL 2011 Student Session, pages 81–87, Portland,
OR, USA. Association for Computational Linguistics.
Bahrainian, S.-A. and Dengel, A. (2013). Sentiment analy-
sis and summarization of twitter data. In 2013 IEEE
16th International Conference on Computational Sci-
ence and Engineering, pages 227–234. IEEE.
Beltagy, I., Lo, K., and Cohan, A. (2019). Scibert: A pre-
trained language model for scientific text. In Proceed-
ings of the 2019 Conference on Empirical Methods
in Natural Language Processing and the 9th Inter-
national Joint Conference on Natural Language Pro-
cessing (EMNLP-IJCNLP), pages 3606–3611.
Cliche, M. (2017). BB twtr at SemEval-2017 task 4: Twit-
ter sentiment analysis with CNNs and LSTMs. In Pro-
ceedings of the 11th International Workshop on Se-
mantic Evaluation (SemEval-2017), pages 573–580,
Vancouver, Canada. Association for Computational
Linguistics.
Cohan, A., Ammar, W., van Zuylen, M., and Cady, F.
(2019). Structural scaffolds for citation intent clas-
sification in scientific publications. arXiv preprint
arXiv:1904.01608.
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., and
Salakhutdinov, R. (2019). Transformer-xl: Attentive
language models beyond a fixed-length context. arXiv
preprint arXiv:1901.02860.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.
(2018). Bert: Pre-training of deep bidirectional trans-
formers for language understanding. arXiv preprint
arXiv:1810.04805.
Esuli, A. and Sebastiani, F. (2006). Determining term sub-
jectivity and term orientation for opinion mining. In
11th Conference of the European Chapter of the As-
sociation for Computational Linguistics.
Feldman, R. (2013). Techniques and applications for
sentiment analysis. Communications of the ACM,
56(4):82–89.
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma,
P., and Soricut, R. (2019). Albert: A lite bert for
self-supervised learning of language representations.
arXiv preprint arXiv:1909.11942.
Lin, C. and He, Y. (2009). Joint sentiment/topic model for
sentiment analysis. In Proceedings of the 18th ACM
conference on Information and knowledge manage-
ment, pages 375–384.
Medhat, W., Hassan, A., and Korashy, H. (2014). Sentiment
analysis algorithms and applications: A survey. Ain
Shams engineering journal, 5(4):1093–1113.
Mercier, D., Bhardwaj, A., Dengel, A., and Ahmed, S.
(2019). Senticite: An approach for publication sen-
timent analysis. arXiv preprint arXiv:1910.03498.
Munikar, M., Shakya, S., and Shrestha, A. (2019). Fine-
grained sentiment classification using bert. In 2019
Artificial Intelligence for Transforming Business and
Society (AITB), volume 1, pages 1–5.
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., and Qin,
B. (2014). Learning sentiment-specific word embed-
ding for twitter sentiment classification. In Proceed-
ings of the 52nd Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers),
pages 1555–1565.
Thongtan, T. and Phienthrakul, T. (2019). Sentiment classi-
fication using document embeddings trained with co-
sine similarity. In Proceedings of the 57th Annual
Meeting of the Association for Computational Lin-
guistics: Student Research Workshop, pages 407–414,
Florence, Italy. Association for Computational Lin-
guistics.
Wu, Z., Rao, Y., Li, X., Li, J., Xie, H., and Wang, F. L.
(2015). Sentiment detection of short text via prob-
abilistic topic modeling. In International Confer-
ence on Database Systems for Advanced Applications,
pages 76–85. Springer.
Xie, Q., Dai, Z., Hovy, E. H., Luong, M., and Le, Q. V.
(2019). Unsupervised data augmentation. CoRR,
abs/1904.12848.
Xu, J., Zhang, Y., Wu, Y., Wang, J., Dong, X., and Xu,
H. (2015). Citation sentiment analysis in clinical trial
papers. In AMIA annual symposium proceedings, vol-
ume 2015, page 1334. American Medical Informatics
Association.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov,
R. R., and Le, Q. V. (2019). Xlnet: Generalized au-
toregressive pretraining for language understanding.
In Advances in neural information processing sys-
tems, pages 5754–5764.
Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., and Xu,
B. (2016). Attention-based bidirectional long short-
term memory networks for relation classification. In
Proceedings of the 54th annual meeting of the associ-
ation for computational linguistics (volume 2: Short
papers), pages 207–212.
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