49
References
[1] Yoshida, Y., Hirao, T., Iwata, T., Nagata, M. and Matsumoto, Y. (2011). Transfer
learning for multiple-domain sentiment analysis — identifying domain
dependent/independent word polarity. [online] Dl.acm.org. Available at:
https://dl.acm.org/citation.cfm?id=2900627 [Accessed 25 Jan. 2019].
[2] Pan, S. J. and Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on
Knowledge and Data Engineering, 22(10):1345–1359
[3] Pan, Sinno Jialin, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. Cross-domain
sentiment classification via spectral feature alignment. in Proceedings of International
Conference on World Wide Web (WWW-2010). 2010.
[4] B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in
Information Retrieval, 2(1-2):1–135, 2008.
[5] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine
learning techniques. In Proceedings of the Conference on Empirical Methods in Natural
Language Processing, pages 79–86, 2002.
[6] Roark and M. Bacchiani. 2003. Supervised and unsupervised PCFG adaptation to novel
domains. In NAACL-HLT, pages 126–133.
[7] Daum´e III and D. Marcu. 2006. Domain adaptation for statistical classifiers. Journal of
Artificial Intelligence Research, 26(1):101–126.
[8] R.K. Ando and T. Zhang. 2005. A framework for learning predictive structures from multiple
tasks and unlabeled data. The Journal of Machine Learning Research, 6:1817–1853.
[9] J. Blitzer, M. Dredze, and F. Pereira. 2007. Biographies, bollywood, boom-boxes and
blenders: Domain adaptation for sentiment classification. In ACL, page 440–447.
[10] John Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with
structural correspondence learning. In Empirical Methods in Natural Language Processing
(EMNLP).
[11] H. Daum´e. 2007. Frustratingly easy domain adaptation. In ACL, pages 256–263.
50
[12] W. Dai, Y. Chen, G.R. Xue, Q. Yang, and Y. Yu. 2008. Translated learning: Transfer
learning across different feature spaces. In NIPS, pages 353–360.
[13] N. Bel, C. Koster, and M. Villegas. Cross-lingual text categorization. In ECDL, 2003.
[14] W. Dai, O. Jin, G.R. Xue, Q. Yang, and Y. Yu. 2009. Eigentransfer: a unified framework
for transfer learning. In ICML, pages 193–200.
[15] S. Thrun and L. Pratt, Eds., Learning to learn. Norwell, MA, USA: Kluwer Academic
Publishers, 1998.
[16] Q. Wu, S. Tan, and X. Cheng. 2009. Graph ranking for sentiment transfer. In ACL-
IJCNLP, pages 317–320.
[17] Q. Wu, S. Tan, X. Cheng, and M. Duan. 2010. MIEA: a Mutual Iterative Enhancement
Approach for Cross-Domain Sentiment Classification. In COLING, page 1327-1335.
[18] C.W. Seah, I. Tsang, Y.S. Ong, and K.K. Lee. 2010. Predictive Distribution Matching SVM
for Multi-domain Learning. In ECML-PKDD, pages 231–247.
[19] Guerra, P., Veloso, A., Meira Jr., W., Almeida, V.: From bias to opinion: A transfer-
learning
approach to real-time sentiment analysis. In: Proceedings of the 17th ACM SIGKDD
Conference on Knowledge Discovery and Data Mining, KDD (2011).
[20] Q. You, J. Luo, H. Jin, J. Yang, Robust image sentiment analysis using progressively
trained and domain transferred deep networks, arXivpreprint arXiv:1509.06041.
[21] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Domain adaptation for large-scale
sentiment classification: A deep learning approach. In ICML, 513–520, 2011.
[22] Chinchung Chang and Chinjen Lin. 2001. LIBSVM: a library for support vector machines.
http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[23] Thorsten Joachims. 1999. Transductive inference for text classification using support vector
machines. In Proceedings of ICML.
[24] Bruzzone, L., Marconcini, M.: Domain adaptation problems: A dasvm classification
technique and a circular validation strategy. IEEE Trans. on PAMI 32(5), 770–787 (2010).
51
[25] Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for
learning from labeled and unlabeled examples. JMLR 12, 2399–2434 (2006).
[26] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, R. Harshman. Indexing by
latent semantic analysis. Journal of the American Society for Information Science, 41:391–407,
1990.
[27] Danushka Bollegala, David Weir, and John Carroll. 2011. Using multiple sources to
construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:
Human Language Technologies, pages 132–141, Portland, Oregon. ACL.
[28] Rajesh, M., and J. M. Gnanasekar. "Annoyed Realm Outlook Taxonomy Using Twin
Transfer Learning" International Journal of Pure and Applied Mathematics, 116.21 (2017) 547-
558.
[29] Alam, F.; Ofli, F.; and Imran, M. 2018. Crisismmd: Multimodal twitter datasets from
natural disasters. In Twelfth International AAAI Conference on Web and Social Media.
[30] Neha Singh, Nirmalya Roy, Aryya Gangopadhyay. “Localized Flood DetectionWith
Minimal Labeled Social Media Data Using Transfer Learning”.
[31] Meysam Asgari-Chenaghlu, Mohammad-Reza Feizi-Derakhshi, Leili
farzinvash, Mohammad-Ali Balafar, Cina Motamed. TopicBERT: A Transformer transfer
learning based memory-graph approach for multimodal streaming social media topic detection
[32] Senevirathne, L., Demotte, P., Karunanayake, B., Udyogi, M. and Ranathunga, S.,
2021. Sentiment Analysis of Sinhala News Comments using Sentence-State LSTM Networks.
[online] Ieeexplore.ieee.org. Available at:
<https://ieeexplore.ieee.org/iel7/9179991/9185188/09185327.pdf> [Accessed 6 April 2021].
[33] Isuru Udara Liyanage. 2018. Sentiment Analysis of Sinhala News Comments. (2018).
Unpublished.
[34] Nishantha Medagoda. 2016. Sentiment Analysis on Morphologically Rich Languages: An
Artificial Neural Network (ANN) Approach. In Artificial Neural Network Modelling. Springer,
377–393.
[35] Nishantha Medagoda. 2017. Framework for Sentiment Classification for Morphologically
Rich Languages: A Case Study for Sinhala. Ph.D. Dissertation. Auckland University of
Technology
52
[36] PDT Chathuranga, SAS Lorensuhewa, and MAL Kalyani. 2019. Sinhala Sentiment
Analysis using Corpus based Sentiment Lexicon. In International Conference on Advances in
ICT for Emerging Regions (ICTer), Vol. 1. 7.
[37] Nafissa Yussupova, Diana Bogdanova. 2012. Applying of Sentiment Analysis for
Texts in Russian Based on Machine Learning Approach.
[38] Muhammad Haroon Shakeel, Safi Faizullah, Turki Alghamidi , Imdadullah Khan.
2019. Language Independent Sentiment Analysis.
[39] Joshi, Aditya, A. R. Balamurali, and Pushpak Bhattacharyya. "A fall-back strategy
for sentiment analysis in hindi: a case study."Proceedings of the 8th ICON (2010).
[40] Bakliwal, Akshat, Piyush Arora, and Vasudeva Varma. "Hindi subjective lexicon:
A lexical resource for hindi polarity classification." In Proceedings of the Eight
International Conference on Language Resources and Evaluation (LREC), pp. 1189-
1196. 2012
[41] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine
translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473,
2014.
[42] Enkhbold Bataa , Joshua Wu . 2019. “An Investigation of Transfer Learning-Based
Sentiment Analysis in Japanese
[43] Marco Pota, Mirko Ventura, Rosario Catelli, and Massimo Esposito. 2020. An
Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian
[44] Abrhalei Frezghi Tela, 2020. Sentiment Analysis for Low-Resource Language: The
Case of Tigrinya
[45] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-
training of deep bidirectional transformers for language understanding. arXiv preprint
arXiv:1810.04805, 2018.
[46] http://mccormickml.com/2019/07/22/BERT-fine-tuning/]
53
[47] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy,
Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert
pretraining approach.
[48] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N
Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, volume 30. Curran Associates, Inc
[49] Feng, F., Yang, Y., Cer, D., Arivazhagan, N., and Wang, W. (2020). Language-agnostic
bert sentence embedding. A
[50] Quoc Thai Nguyen, Thoai Linh Nguyen, Ngoc Hoang Luong, and Quoc Hung Ngo. Fine-
Tuning BERT for Sentiment Analysis of Vietnamese Reviews, 2020 - ieeexplore.ieee.org
[51] Soroush Karimi, Fatemeh Sadat Shahrabadi. 2019. Sentiment analysis using BERT
(pre-training language representations) and Deep Learning on Persian texts.
[52] Alduailej, A., Alothaim, A. AraXLNet: pre-trained language model for sentiment
analysis of Arabic. J Big Data 9, 72 (2022). https://doi.org/10.1186/s40537-022-00625-z
[53] Tsuruoka Y. Deep learning and natural language processing. Brain Nerve
2022;71:45–55
[54] https://github.com/ysenarath/sinling
[55]Matej Ulˇcar and Marko Robnik-Sikonja. High quality elmo embeddings ˇ for seven
less-resourced languages. In Proceedings of The 12th Language Resources and
Evaluation Conference, pages 4731–4738, Marseille, France, May 2020. European
Language Resources Association.