社群網站為世界各地的使用者提供一個能透過短文字交流、分享見聞的平台。同時也給予研究者探索在不同文化背景下，人們所使用情緒語言差異的機會。然而，情緒往往藏在各種語言情境與枝微末節中，故使得欲從字裡行間擷取情緒表現、情緒特徵的研究者或運算系統面臨諸多挑戰。

在本篇研究中，我們提出一個情緒辨識的框架，它權衡傳統的資訊檢索方法與基於類神經的作法，並進一步整合為一。更確切地說，我們建構一個模型，它係以圖形理論作為基礎並延伸，能夠自情緒文本中萃取豐富的語意樣式；接著，透過能增強特徵之間的語意關係的類神經詞嵌入方法，豐富所提取之基於樣式的特徵。

我們所提出的框架，在兼容並蓄傳統的方法後，具備更完整良好的特質，例如模型的可解釋性、使用彈性以及概括性。此外，在本篇研究中，我們亦探究現階段自然語言處理的遷移式學習系統在情緒辨識任務上所扮演的角色；並且，我們提供了在不同情緒識別任務中，本篇模型與其他多種基準模型的實驗結果。最後，透過探討方法在未來的研究價值與改進方向作結，提供有興趣的研究者作為參考。

Social media platforms provide a means for people from all over the world to communicate and share opinions via short and concise text messages. This communication medium has provided a way for researchers to investigate the use of emotional language on social networks across different cultural groups. Emotional expressions are conveyed with all sorts of linguistic phenomena and nuances that present various challenges for computational systems that aim to extract emotion from textual information through various feature representations. In this work, we propose a unified framework for emotion recognition that leverages different traditional information retrieval methods and neural based approaches. Specifically, we propose a graph-based mechanism to extract rich syntactic patterns from an emotion corpus. Thereafter, the extracted pattern-based features are enriched with semantic information through a neural word embedding approach that aims to enhance the semantic relationship among the features. By combining these approaches, the proposed emotion recognition system offers desired characteristics such as explainability, flexibility, coverage, among others. Moreover, we explore and evaluate modern natural language processing transfer learning systems and discuss the role they play in emotion recognition. We include several baseline models, propose several benchmarks, and provide empirical results for several emotion recognition tasks. Lastly, we discuss future work and offer recommendations to further improve text-based emotion recognition systems.

ntroduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 What are Emotions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 Overview and Organization . . . . . . . . . . . . . . . . . . . . . . . . 7
Related Work 9
2.1 Psychological Models and Emotion Corpus . . . . . . . . . . . . . . . 9
2.2 Traditional Methods for Emotion Recognition . . . . . . . . . . . . . . 10
2.3 Modern Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.1 Sequence Modeling . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.2 Transfer Learning and Language Modeling . . . . . . . . . . . 12
Overview of Text Classification Methods 14
3.1 Text Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Machine Learning for Text Classification . . . . . . . . . . . . . . . . 15
3.3 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.1 Activation Functions . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.2 RNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3.3 LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3.4 GRNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.5 Bidirectional RNN . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.6 CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4.1 ELMo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4.2 ULMFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4.3 OpenAI GPT and BERT . . . . . . . . . . . . . . . . . . . . . 28
Data 30
4.1 Distant Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.2 Data Quality and Quantity . . . . . . . . . . . . . . . . . . . . . . . . 31
Traditional Approaches for Text-Based Emotion Recognition in Social Media 33
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.2.1 Character n-grams . . . . . . . . . . . . . . . . . . . . . . . . 34
5.2.2 Bag of Words and Word n-grams . . . . . . . . . . . . . . . . . 35
5.2.3 TF-IDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2.4 LIWC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.3.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . 36
5.3.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 39
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Graph-Based Pattern Extraction for Text-Based Emotion Recognition in Social
Media 44
6.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6.2 Pattern-Based Feature Construction . . . . . . . . . . . . . . . . . . . 45
6.2.1 Step 1 (Normalization) . . . . . . . . . . . . . . . . . . . . . . 46
6.2.2 Step 2 (Graph Construction) . . . . . . . . . . . . . . . . . . . 46
6.2.3 Step 3 (Graph Aggregation) . . . . . . . . . . . . . . . . . . . 46
6.2.4 Step 4 (Token Categorization) . . . . . . . . . . . . . . . . . . 47
6.2.5 Step 5 (Pattern Candidates) . . . . . . . . . . . . . . . . . . . . 49
6.2.6 Step 6 (Basic Pattern Extraction) . . . . . . . . . . . . . . . . . 49
6.3 Pattern Weighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
6.3.1 Pattern Frequency . . . . . . . . . . . . . . . . . . . . . . . . 51
6.3.2 Inverse Emotion Frequency . . . . . . . . . . . . . . . . . . . 51
6.3.3 Pattern Frequency-Inverse Emotion Frequency . . . . . . . . . 51
6.4 Vector Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.5 CNN Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Enhancing Syntactic Representations with Semantic Information for Emotion
Recognition 60
7.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
7.2 Enhanced Contextualized Representations . . . . . . . . . . . . . . . . 61
7.3 Pattern Enrichment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
7.3.1 Pre-trained Word Embeddings . . . . . . . . . . . . . . . . . . 62
7.3.2 Word Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
7.3.3 Enriched-Pattern Construction . . . . . . . . . . . . . . . . . . 62
7.3.4 Emotion Pattern Weighting . . . . . . . . . . . . . . . . . . . . 65
7.4 Pattern Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
7.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
7.5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . 66
7.5.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 69
7.6 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.6.1 Pattern Coverage and Consistency . . . . . . . . . . . . . . . . 78
7.6.2 What’s captured by the proposed model? . . . . . . . . . . . . 80
7.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Conclusion 85
References 86
Appendix 96
9.1 Hyperparameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
9.2 Architecture Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

[1] Robert Plutchik. The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American Scientist, 89(4):344–350, 2001.
[2] James Russell. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161–1178, 1980.
[3] Jeremy Howard and Sebastian Ruder. Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 328–339, 2018.
[4] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998–6008, 2017.
[5] Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of Empirical Methods in Natural Language Processing, pages 79–86. ACL, 2002.
[6] Alec Go, Richa Bhayani, and Lei Huang. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 2009.
[7] Alexander Pak and Patrick Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. In LREC, 2010.
[8] Bing Liu and Lei Zhang. A survey of opinion mining and sentiment analysis. In Mining Text Data, pages 415–463. Springer, 2012.
[9] Xia Hu, Jiliang Tang, Huiji Gao, and Huan Liu. Unsupervised sentiment analysis with emotional signals. In Proceedings of the 22nd international conference on World Wide Web, pages 607–618. ACM, 2013.
[10] Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, and Bing Qin. Learning sentiment-specific word embedding for twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1555–1565, 2014.
[11] Rosalind Wright Picard et al. Affective computing. MIT Press, 1995.
[12] Erik Cambria, Andrew Livingstone, and Amir Hussain. The hourglass of emotions. In Cognitive behavioural systems, pages 144–157. Springer, 2012.
[13] Erik Cambria. Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2):102–107, 2016.
[14] Paul Ekman. An argument for basic emotions. Cognition & Emotion, 6(3-4):169–200, 1992.
[15] Karin Becker, Viviane Moreira, and Aline G.L. dos Santos. Multilingual emotion classification using supervised learning: Comparative experiments. Information Processing & Management, 53(3):684–704, 2017.
[16] Svitlana Volkova and Yoram Bachrach. Inferring perceived demographics from user emotional tone and user-environment emotional contrast. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1567–1578. ACL, 2016.
[17] Munmun De Choudhury, Scott Counts, and Michael Gamon. Not all moods are created equal! exploring human emotional states in social media. In Sixth international AAAI conference on weblogs and social media, 2012.
[18] Chun-Hao Chang, Elvis Saravia, and Yi-Shin Chen. Subconscious crowdsourcing: A feasible data collection mechanism for mental disorder detection on social media. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2016, pages 374–379. ACM, 2016.
[19] Elvis Saravia, Chun-Hao Chang, Renaud Jollet De Lorenzo, and Yi-Shin Chen. Midas: Mental illness detection and analysis via social media. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2016, pages 1418–1421. ACM, 2016.
[20] Hong-Han Shuai, Chih-Ya Shen, De-Nian Yang, Yi-Feng Carol Lan, Wang-Chien Lee, S Yu Philip, and Ming-Syan Chen. A comprehensive study on social network mental disorders detection via online social media mining. IEEE Transactions on Knowledge and Data Engineering, 30(7):1212–1225, 2018.
[21] Elvis Saravia, Carlos Argueta, and Yi-Shin Chen. Emoviz: Mining the world’s interest through emotion analysis. In Advances in Social Networks Analysis and Mining (ASONAM), 2015 IEEE/ACM International Conference on, pages 753–756. IEEE, 2015.
[22] Fernando Calderon, Chun-Hao Chang, Carlos Argueta, Elvis Saravia, and Yi-Shin Chen. Analyzing event opinion transition through summarized emotion visualization. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pages 749–752. ACM, 2015.
[23] Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, and Erik Cambria. Dialoguernn: An attentive rnn for emotion detection in conversations. arXiv preprint arXiv:1811.00405, 2018.
[24] Saif Mohammad. # emotional tweets. In Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pages 246–255. ACL, 2012.
[25] Jared Suttles and Nancy Ide. Distant supervision for emotion classification with discrete binary values. In International Conference on Intelligent Text Processing and Computational Linguistics, pages 121–136. Springer, 2013.
[26] Ben Eisner, Tim Rockt¨aschel, Isabelle Augenstein, Matko Boˇsnjak, and Sebastian Riedel. emoji2vec: Learning emoji representations from their description. arXiv preprint arXiv:1609.08359, 2016.
[27] Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1615–1625. ACL, 2017.
[28] Saif Mohammad and Svetlana Kiritchenko. Using hashtags to capture fine emotion categories from tweets. Computational Intelligence, 31(2):301–326, 2015.
[29] Jasy Suet Yan Liew and Howard R Turtle. Exploring fine-grained emotion detection in tweets. In Proceedings of the NAACL Student Research Workshop, pages 73–80, 2016.
[30] Muhammad Abdul-Mageed and Lyle Ungar. Emonet: Fine-grained emotion detection with gated recurrent neural networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 718–728. ACL, 2017.
[31] Carlo Strapparava and Rada Mihalcea. Semeval-2007 task 14: Affective text. In Proceedings of the 4th International Workshop on Semantic Evaluations, pages 70–74. ACL, 2007.
[32] Valentina Sintsova, Claudiu-Cristian Musat, and Pearl Pu. Fine-grained emotion recognition in olympic tweets based on human computation. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 12–20, 2013.
[33] Jonathon Read. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL student research workshop, pages 43–48. ACL, 2005.
[34] Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pages 1003–1011. ACL, 2009.
[35] Roberto Gonz´alez-Ib´anez, Smaranda Muresan, and Nina Wacholder. Identifying sarcasm in twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 581–586. ACL, 2011.
[36] Matthew Purver and Stuart Battersby. Experimenting with distant supervision for emotion classification. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 482–491. ACL, 2012.
[37] WenboWang, Lu Chen, Krishnaprasad Thirunarayan, and Amit Sheth. Harnessing twitter “big data” for automatic emotion identification. In Proceedings of Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), pages 587–592. IEEE, 2012.
[38] Minsu Park, Chiyoung Cha, and Meeyoung Cha. Depressive moods of users portrayed in twitter. In Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD), pages 1–8. ACM, 2012.
[39] Munmun De Choudhury, Scott Counts, and Eric Horvitz. Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3267–3276. ACM, 2013.
[40] Glen Coppersmith, Craig Harman, and Mark Dredze. Measuring post traumatic stress disorder in twitter. In ICWSM, 2014.
[41] Glen Coppersmith, Mark Dredze, and Craig Harman. Quantifying mental health signals in twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 51–60, 2014.
[42] James Pennebaker, Roger Booth, and Martha Francis. Linguistic inquiry and word count: Liwc [computer software]. Austin, TX: liwc. net, 2007.
[43] Carlo Strapparava and Alessandro Valitutti. Wordnet affect: an affective extension of wordnet. In LREC, pages 1083–1086, 2004.
[44] Saif Mohammad and Peter Turney. Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3):436–465, 2013.
[45] John Blitzer, Mark Dredze, and Fernando Pereira. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeeting of the Association of Computational Linguistics, pages 440–447. ACL, 2007.
[46] Kirk Roberts, Michael Roach, Joseph Johnson, Josh Guthrie, and Sanda Harabagiu. Empatweet: Annotating and detecting emotions on twitter. In LREC, pages 3806–3813, 2012.
[47] Ashequl Qadir and Ellen Riloff. Bootstrapped learning of emotion hashtags# hashtags4you. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 2–11, 2013.
[48] Svitlana Volkova, Theresa Wilson, and David Yarowsky. Exploring sentiment in social media: Bootstrapping subjectivity clues from multilingual twitter streams. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 505–510. ACL, 2013.
[49] Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In Proceedings of Neural Information Processing Systems, pages 649–657, 2015.
[50] Yoon Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014.
[51] Bernhard Kratzwald, Suzana Ilic, Mathias Kraus, Stefan Feuerriegel, and Helmut Prendinger. Decision support with text-based emotion recognition: Deep learning for affective computing. arXiv preprint arXiv:1803.06397, 2018.
[52] Sepp Hochreiter and J¨urgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
[53] Kyunghyun Cho, Bart Van Merri¨enboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.
[54] Jeffrey Elman. Finding structure in time. Cognitive science, 14(2):179–211, 1990.
[55] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
[56] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. ACL, 2014.
[57] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
[58] Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.
[59] Tom Mitchell, Bruce Buchanan, Gerald DeJong, Thomas Dietterich, Paul Rosenbloom, and Alex Waibel. Machine learning. Annual review of computer science, 4(1):417–433, 1990.
[60] Mike Schuster and Kuldip Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681, 1997.
[61] Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. IEEE, 2013.
[62] Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, pages 160–167. ACM, 2008.
[63] Soujanya Poria, Iti Chaturvedi, Erik Cambria, and Amir Hussain. Convolutional mkl based multimodal emotion recognition and sentiment analysis. In Proceedings of Data Mining (ICDM), 2016 IEEE 16th International Conference on, pages 439–448. IEEE, 2016.
[64] Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin, and Zhiyuan Liu. Neural sentiment classification with user and product attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1650–1659, 2016.
[65] Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, and Chandan K Reddy. Neural abstractive text summarization with sequence-to-sequence models. arXiv preprint arXiv:1812.02303, 2018.
[66] Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. URL https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/languageunsupervised/languageunderstandingpaper.pdf, 2018.
[67] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843, 2016.
[68] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
[69] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[70] HaraldWallbott and Klaus Scherer. How universal and specific is emotional experience? evidence from 27 countries on five continents. Information (International Social Science Council), pages 763–795, 1986.
[71] Vivi Nastase, Marina Sokolova, and Jelber Shirabad. Do happy words sound happy? a study of the relation between form and meaning for english words expressing emotions. Proceedings of Recent Advances in Natural Language Processing (RANLP 2007), pages 406–410, 2007.
[72] Takuma Igarashi, Ryohei Sasano, Hiroya Takamura, and Manabu Okumura. Use of sound symbolism in sentiment classification. Journal of Natural Language Processing, 20(2):183–200, 2013.
[73] Margaret Bradley and Peter Lang. Affective norms for english words (anew): Instruction manual and affective ratings. Technical report, Technical report C-1, the center for research in psychophysiology, University of Florida, 1999.
[74] Oscar Araque, Lorenzo Gatti, Jacopo Staiano, and Marco Guerini. Depechemood++: a bilingual emotion lexicon built through simple yet powerful techniques. arXiv preprint arXiv:1810.03660, 2018.
[75] Saif Mohammad and Peter Turney. Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, pages 26–34. Association for Computational Linguistics, 2010.
[76] Elvis Saravia, Carlos Argueta, and Yi-Shin Chen. Unsupervised graph-based pattern extraction for multilingual emotion classification. Social Network Analysis and Mining, 6(1):92, 2016.
[77] Cindy Chung and James Pennebaker. The psychological functions of function words. Social communication, 1:343–359, 2007.
[78] PS Sreeja and GS Mahalaksmi. Applying vector space model for poetic emotion recognition. Advances in Natural and Applied Sciences, 9(6 SE):486–491, 2015.
[79] Taner Danisman and Adil Alpkocak. Feeler: Emotion classification of text using vector space model. In AISB 2008 Convention Communication, Interaction and Social Intelligence, page 53, 2008.
[80] Vinod Nair and Geoffrey Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010.
[81] Y-Lan Boureau, Jean Ponce, and Yann LeCun. A theoretical analysis of feature pooling in visual recognition. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 111–118, 2010.
[82] Geoffrey Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
[83] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[84] Jan Deriu, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon M¨uller, Mark Cieliebak, Thomas Hofmann, and Martin Jaggi. Leveraging large amounts of weakly supervised data for multi-language sentiment classification. In Proceedings of the 26th International Conference on World Wide Web, pages 1045–1052.
International World Wide Web Conferences Steering Committee, 2017.
[85] Joe Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301):236–244, 1963.
[86] Febian Pedregosa, Ga¨el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
[87] Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge university press, 2014.
[88] Yoav Goldberg. A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research, 57:345–420, 2016.
[89] Kenneth Ward Church and Patrick Hanks. Word association norms, mutual information, and lexicography. Computational linguistics, 16(1):22–29, 1990.
[90] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 2017.