Abstract
Emojis are frequently used to express moods, emotions, and feelings in social media. There has been much research on emojis and sentiments. However, existing methods mainly face two limitations. First, they treat emojis as binary indicator features and rely on handcrafted features for emoji-based sentiment analysis. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this article, we investigate a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work. First, it does not need feature engineering. Second, it utilizes the attention approach to model the impact of emojis on text. An evaluation on 10,042 manually labeled Sina Weibo showed that our model achieves much better performance compared with several strong baselines. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/emoji.
- Sadam Al-Azani and El-Sayed El-Alfy. 2018. Emojis-based sentiment classification of Arabic microblogs using deep recurrent neural networks. In Proceedings of the International Conference on Computing Sciences and Engineering (ICCSE’18). IEEE, Los Alamitos, CA, 1--6.Google Scholar
Cross Ref
- Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados, Steven Schockaert, and Horacio Saggion. 2018. Interpretable emoji prediction via label-wise attention LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4766--4771.Google Scholar
Cross Ref
- Naomi S. Baron. 2009. The myth of impoverished signal: Dispelling the spoken language fallacy for emoticons in online communication. In Electronic Emotion: The Mediation of Emotion via Information and Communication Technologies. Peter Lang, 107–135.Google Scholar
- Ghazaleh Beigi, Xia Hu, Ross Maciejewski, and Huan Liu. 2016. An overview of sentiment analysis in social media and its applications in disaster relief. In Sentiment Analysis and Ontology Engineering. Springer, 313--340.Google Scholar
- Johan Bollen, Huina Mao, and Alberto Pepe. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. InProceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM’11). 450--453.Google Scholar
- Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin, and Zhiyuan Liu. 2016. Neural sentiment classification with user and product attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 1650--1659.Google Scholar
Cross Ref
- Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 452--461.Google Scholar
Cross Ref
- Yuxiao Chen, Jianbo Yuan, Quanzeng You, and Jiebo Luo. 2018. Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In Proceedings of the 2018 ACM Conference on Multimedia (CM’18). 117--125.Google Scholar
Digital Library
- Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, and Xuanzhe Liu. 2019. Emoji-powered representation learning for cross-lingual sentiment classification. In Proceedings of the World Wide Web Conference. ACM, New York, NY, 251–262.Google Scholar
Digital Library
- Xing Fang and Justin Zhan. 2015. Sentiment analysis using product review data. Journal of Big Data 2, 1 (2015), 5.Google Scholar
Cross Ref
- Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter Sentiment Classification Using Distant Supervision. CS224N Project Report. Stanford University.Google Scholar
- Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18, 5--6 (2005), 602--610.Google Scholar
Digital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780.Google Scholar
Digital Library
- Tianran Hu, Han Guo, Hao Sun, Thuy-Vy Thi Nguyen, and Jiebo Luo. 2017. Spice up your chat: The intentions and sentiment effects of using emoji. arXiv:1703.02860.Google Scholar
- Xia Hu, Lei Tang, Jiliang Tang, and Huan Liu. 2013. Exploiting social relations for sentiment analysis in microblogging. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, New York, NY, 537--546.Google Scholar
Digital Library
- Fei Jiang, Yiqun Liu, Huanbo Luan, Min Zhang, and Shaoping Ma. 2014. Microblog sentiment analysis with emoticon space model. In Proceedings of the Chinese National Conference on Social Media Processing. 76--87.Google Scholar
Cross Ref
- Fei Jiang, Yi-Qun Liu, Huan-Bo Luan, Jia-Shen Sun, Xuan Zhu, Min Zhang, and Shao-Ping Ma. 2015. Microblog sentiment analysis with emoticon space model. Journal of Computer Science and Technology 30, 5 (2015), 1120–1129.Google Scholar
Cross Ref
- Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016. Text understanding with the attention sum reader network. arXiv:1603.01547.Google Scholar
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv:1408.5882.Google Scholar
- Mayu Kimura and Marie Katsurai. 2017. Automatic construction of an emoji sentiment lexicon. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, New York, NY, 1033--1036.Google Scholar
Digital Library
- Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. 2014. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research 50 (2014), 723--762.Google Scholar
Cross Ref
- Tuan Anh Le, David Moeljadi, Yasuhide Miura, and Tomoko Ohkuma. 2016. Sentiment analysis for low resource languages: A study on informal Indonesian tweets. In Proceedings of the 12th Workshop on Asian Language Resources (ALR-12). 123--131.Google Scholar
- Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Claypool.Google Scholar
- Jiangming Liu and Yue Zhang. 2016. Shift-reduce constituent parsing with neural lookahead features. arXiv:1612.00567.Google Scholar
- Jiangming Liu and Yue Zhang. 2017. Attention modeling for targeted sentiment. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. 572--577.Google Scholar
Cross Ref
- Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, and Chu Ren Huang. 2017. A cognition based attention model for sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.Google Scholar
Cross Ref
- Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv:1508.04025.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119.Google Scholar
- Saif M. Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. 2013. NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In Proceedings of the Joint Conference on Lexical and Computational Semantics. 321–327.Google Scholar
- Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. 2016. SemEval-2016 Task 4: Sentiment analysis in Twitter. In Proceedings of the International Workshop on Semantic Evaluation. 1--18.Google Scholar
Cross Ref
- Sascha Narr, Michael Hulfenhaus, and Sahin Albayrak. 2012. Language-independent Twitter sentiment analysis. In Proceedings of the Learning, Knowledge, and Adaption Conference (LWA’12). 12--14.Google Scholar
- Petra Kralj Novak, Jasmina Smailović, Borut Sluban, and Igor Mozetič. 2015. Sentiment of emojis. PLoS One 10, 12 (2015), e0144296.Google Scholar
- Debora Nozza, Elisabetta Fersini, and Enza Messina. 2017. A multi-view sentiment corpus. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 273--280.Google Scholar
Cross Ref
- Alexander Pak and Patrick Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC’10). 1320--1326.Google Scholar
- Yafeng Ren, Donghong Ji, and Han Ren. 2018. Context-augmented convolutional neural networks for Twitter sarcasm detection. Neurocomputing 308 (2018), 1–7.Google Scholar
Digital Library
- 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 (SemEval’15). 451--463.Google Scholar
Cross Ref
- Jayashree Subramanian, Varun Sridharan, Kai Shu, and Huan Liu. 2019. Exploiting emojis for sarcasm detection. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, and Behavior Representation in Modeling and Simulation. 70--80.Google Scholar
Cross Ref
- Ming Tan, Cicero dos Santos, Bing Xiang, and Bowen Zhou. 2016. Improved representation learning for question answer matching. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Volume 1, Long Papers. 464--473.Google Scholar
Cross Ref
- Duyu Tang, Bing Qin, and Ting Liu. 2015. Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1422--1432.Google Scholar
Cross Ref
- Ye Tian, Thiago Galery, Giulio Dulcinati, Emilia Molimpakis, and Chao Sun. 2017. Facebook sentiment: Reactions and emojis. In Proceedings of the 5th International Workshop on Natural Language Processing for Social Media. 11--16.Google Scholar
Cross Ref
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998--6008.Google Scholar
- Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015. Grammar as a foreign language. In Advances in Neural Information Processing Systems. 2773--2781.Google Scholar
- Joseph B. Walther and Kyle P. ’Addario. 2001. The impacts of emoticons on message interpretation in computer-mediated communication. Social Science Computer Review 19, 3 (2001), 324--347.Google Scholar
Cross Ref
- Xinyu Wang, Chunhong Zhang, Yang Ji, Li Sun, Leijia Wu, and Zhana Bao. 2013. A depression detection model based on sentiment analysis in micro-blog social network. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 201--213.Google Scholar
Digital Library
- Alecia Wolf. 2000. Emotional expression online: Gender differences in emoticon use. CyberPsychology 8 Behavior 3, 5 (2000), 827--833.Google Scholar
- 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. 1480--1489.Google Scholar
Cross Ref
- Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2017. Hierarchical attention networks for document classification. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480--1489.Google Scholar
- Matthew D. Zeiler. 2012. ADADELTA: An adaptive learning rate method. arXiv:1212.5701.Google Scholar
- Lei Zhang, Shuai Wang, and Bing Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8, 4 (2018), e1253.Google Scholar
Cross Ref
- Xinjie Zhou, Xiaojun Wan, and Jianguo Xiao. 2016. Attention-based LSTM network for cross-lingual sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 247--256.Google Scholar
Cross Ref
Index Terms
Emoji-Based Sentiment Analysis Using Attention Networks
Recommendations
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
MM '18: Proceedings of the 26th ACM international conference on MultimediaSentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so ...
Fusion Pre-trained Emoji Feature Enhancement for Sentiment Analysis
Emoji are often used in social media to enrich users’ emotions, and they play an important role in the task of social media sentiment analysis. In practice, researchers are more likely to consider emoji as special symbols and treat them separately from ...
Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog
As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and ...






Comments