|
Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation, pp. 2200-2204. Alena Neviarouskaya, Helmut Prendinger, and Mitsuru Ishizuka. 2011. SentiFul: A lexicon for sentiment analysis, IEEE Transactions on Affective Computing, no. 2, pp. 22-36. Clayton Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI Conference on Web and Social Media, vol. 8, no. 1, pp. 216-225. Erik Cambria, Yang Li, Frank Z Xing, Soujanya Poria, and Kenneth Kwok. 2020. SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 105-114. Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif Mohammad, Alan Ritter, and Veselin Stoyanov. 2015. SemEval-2015 task 10: Sentiment analysis in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 451-463. Svetlana Kiritchenko, Saif Mohammad, and Mohammad Salameh. 2016. Semeval-2016 task 7: Determining sentiment intensity of english and arabic phrases. In Proceedings of the 10th International Workshop on Semantic Evaluation, pp. 42-51. Keith Cortis, André Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh, and Brian Davis. 2017. Semeval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news. In Proceedings of the 11th International Workshop on Semantic Evaluation, pp. 519-535. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631-1642. Dan Klein and Christopher D. Manning. 2003. Accurate Unlexicalized Parsing. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 423-430. Margaret M Bradley and Peter J. Lang. 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. Technical Report C-1, the Center for Research in Psychophysiology, University of Florida. Amy Beth Warriner, Victor Kuperman, and Marc Brysbaert. 2013. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, vol. 45, pp. 1191-1207. Margaret M Bradley and Peter J Lang. 2007. Affective Norms for English Text (ANET): Affective ratings of text and instruction manual, Techical Report. D-1, University of Florida, Gainesville, FL. Daniel Preoţiuc-Pietro, H Andrew Schwartz, Gregory Park, Johannes Eichstaedt, Margaret Kern, Lyle Ungar, and Elisabeth Shulman. 2016. Modelling valence and arousal in facebook posts. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 9-15. Sven Buechel and Udo Hahn. 2017. Emobank: Studying the impact of annotation perspective and representation format on dimensional emotion analysis. arXiv preprint arXiv:2205.01996. Liang-Chih Yu, Lung-Hao Lee, and Kam-Fai Wong. 2016b. Overview of the IALP 2016 shared task on dimensional sentiment analysis for Chinese words, In 2016 International Conference on Asian Language Processing, pp. 156-160. Liang-Chih Yu, Lung-Hao Lee, Jin Wang, and Kam-Fai Wong. 2017. IJCNLP-2017 Task 2: Dimensional sentiment analysis for Chinese phrases, In Proceedings of the 17th International Joint Conference on Natural Language Processing, pp. 9-16. Liang-Chih Yu, Jin Wang, Bo Peng, and Chu-Ren Huang. 2021. ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing, pp. 385-388. Lung-Hao Lee, Jian-Hong Li, and Liang-Chih Yu. 2022. Chinese EmoBank: Building valence-arousal resources for dimensional sentiment analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 4, pp. 1-18. Chin-Lan Huang, Cindy K Chung, Natalie Hui, Yi-Cheng Lin, Yi-Tai Seih, Ben CP Lam, Wei-Chuan Chen, Michael H Bond, and James W Pennebaker. 2012. The development of the Chinese linguistic inquiry and word count dictionary. Chinese Journal of Psychology, vol. 54, no. 2, pp. 185-201. Lun‐Wei Ku, and Hsin‐Hsi Chen. 2007. Mining opinions from the Web: Beyond relevance retrieval. Journal of the American Society for Information Science and Technology, vol. 58, no. 12, pp. 1838-1850. Peter D. Turney and Michael L. Littman. 2002. Unsupervised learning of semantic orientation from a hundred-billion-word corpus. arXiv preprint, arXiv:cs/0212012. Liang-Chih Yu, Jin Wang, K. Robert Lai, and Xue-jie Zhang. 2015. Predicting valence-arousal ratings of words using a weighted graph method. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 2, pp. 788-793. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv preprint, arXiv:1301.3781. Livia Polanyi and Annie Zaenen. 2006. Contextual valence shifters. Computing Attitude and Affect in Text: Theory and Applications, pp. 1-10. Liang-Chih Yu, Jin Wang, K. Robert Lai, and Xuejie Zhang. 2020. Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction. IEEE Transactions on Affective Computing, vol. 11, no. 03, pp. 447-458. Yu-Chih Deng, Cheng-Yu Tsai, Yih-Ru Wang, Sin-Horng Chen, and Lung-Hao Lee. 2022. Predicting Chinese phrase-level sentiment intensity in valence-arousal dimensions with linguistic dependency features, IEEE Access, vol. 10, pp. 126612-126620. Jingjing Liu and Stephanie Seneff. 2009. Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 161-169. Georgios Paltoglou, Mathias Theunis, Arvid Kappas, and Mike Thelwall. 2013. Predicting Emotional Responses to Long Informal Text. IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 106-115. Jin Wang, Liang-Chih Yu, K. Robert Lai, and Xuejie Zhang. 2016. Dimensional sentiment analysis using a regional CNN-LSTM model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 225-230. Yoshua Bengio, Patrice Simard, and Paolo Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166. Md Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, and Pushpak Bhattacharyya. 2017. A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 540–546. Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, vol. 11, no. 12, pp. 3371-3408. Xiaowen Ding, Bing Liu, and Philip S. Yu. 2008. A Holistic Lexicon-Based Approach to Opinion Mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231-240. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase- level Sentiment Analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354. Jin Wang, Liang-Chih Yu, K. Robert Lai, and Xuejie Zhang. 2020. Tree-structured regional CNN-LSTM model for dimensional sentiment analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 581-591. Amal Htait and Leif Azzopardi. 2021. Sentiment intensity prediction using neural word embeddings. Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 93-102. Yu-Chih Deng, Yih-Ru Wang, Sin-Horng Chen, and Lung-Hao Lee. 2023. Towards Transformer Fusions for Chínese Sentiment Intensity Prediction in Valence-Arousal Dimensions, IEEE Access, vol. 11, pp. 109974-109982. Liang Yao Chengsheng Mao, and Yuan Luo. 2019. Graph convolutional networks for text classification. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 7370-7377. Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, and Liang Wang. 2020. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 334-339. Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, and Huan Liu. 2020. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 4927-4936. Chenwei Lou, Bin Liang, Lin Gui, Yulan He, Yixue Dang, and Ruifeng Xu. 2021. Affective dependency graph for sarcasm detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1844-1849. Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung. 2022. Universal graph transformer self-attention networks. In Companion Proceedings of the Web Conference 2022, pp. 193-196. Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint, arXiv:1511.05493. Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based sentiment classification with aspect-specific graph convolutional networks, In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 4568-4578. Margaret M Bradley and Peter J Lang. 1994. Measuring emotion: the self-assessment manikin and the semantic differential, Journal of Behavior Therapy and Experimental Psychiatry, vol. 25, pp. 49-59. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532-1543. Ozan Irsoy and Claire Cardie. 2014. Opinion mining with deep recurrent neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 720-728. Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746-1751. Xin Wang, Yuanchao Liu, Chengjie Sun, Baoxun Wang, and Xiaolong Wang. 2015. Predicting polarities of tweets by composing word embeddings with long short-term memory. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 1343-1353. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480-1489. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, arXiv:1810.04805. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, vol. 5, pp. 135-146.
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