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Emotions During Covid-19: LSTM Models for Emotion Detection in Tweets

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Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 237))

Abstract

Deep learning techniques for emotion detection in micro-blogs are a relatively less explored area of research. This paper investigates the performance of Long Short-Term Memory (LSTM) networks in detecting emotions from English tweets that relate to the Covid-19 pandemic. The two proposed LSTM models viz. Simple LSTM and EmoLex Boost LSTM use a corpus of streaming tweets and train the networks to detect emotions in tweets. Simple LSTM architecture comprises two hidden layers and a fully connected layer with softmax activation. EmoLex Boost LSTM uses the NRC emotion lexicon to enhance the Simple LSTM architecture. Emotion classification experiments were conducted to test both LSTM models. While the Simple LSTM model shows an accuracy of 60.57% when trained for 30 epochs, the EmoLex Boost model shows an enhanced accuracy of 61.75% when trained for 30 epochs, and 63.09% when trained for 50 epochs. Both deep learning models identify emotions in tweets but do not compute their valence. Since a tweet can convey multiple emotions, the annotated emotion labels in the training set tend to be subjective or fuzzy. This adversely impacts the performance scores of models. The results of our experiments, however, are promising and motivate further research in deep learning models that compute the valence of emotion(s).

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References

  1. Kiritchenko S, Mohammad S, Salameh M (2016) Semeval-2016 task 7: determining sentiment intensity of English and Arabic phrases. In: Proceedings of the 10th international workshop on semantic evaluation (SEMEVAL-2016), pp 42–51

    Google Scholar 

  2. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10, pp 79–86. Association for Computational Linguistics. https://arxiv.org/abs/cs/0205070

  3. Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics, pp 271. Association for Computational Linguistics. https://arxiv.org/abs/cs/0409058

  4. Ren Y, Wang R, Ji D (2016) A topic-enhanced word embedding for Twitter sentiment classification. Inf Sci 369:188–198. https://doi.org/10.1016/j.ins.2016.06.040

    Article  Google Scholar 

  5. Ren Y, Zhang Y, Zhang M, Ji D (2016) Improving Twitter sentiment classification using topic-enriched multi-prototype word embeddings. In: AAAI, pp 3038–3044

    Google Scholar 

  6. Ribeiro FN, Araújo M, Gonçalves P, Benevenuto F, Gonçalves MA (2015) SentiBench-a benchmark comparison of state-of-the-practice sentiment analysis methods. arXiv preprint arXiv:1512.01818

  7. Saif H, He Y, Alani H (2012) Semantic sentiment analysis of twitter. In: Cudré-Mauroux P et al. (eds) The semantic web—ISWC 2012. Lecture notes in computer science, vol 7649. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35176-1_32

  8. Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manage 52(1):5–19. https://doi.org/10.1016/j.ipm.2015.01.005

    Article  Google Scholar 

  9. Mohammad SM, Kiritchenko S, Zhu X (2013) NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the seventh international workshop on semantic evaluation exercises (SemEval-2013), Atlanta, Georgia, USA. https://arxiv.org/abs/1308.6242

  10. Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882

  11. Liu Q, Zhou F, Hang R, Yuan X (2017) Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification. CoRR. http://arxiv.org/abs/1703.07910

  12. Ordóñez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1). https://doi.org/10.3390/s16010115

  13. Abdi A, Shamsuddin SM, Hasan S, Piran J (2019) Deep learning-based sentiment classification of evaluative text based on multi-feature fusion. Inf Process Manage 56(4):1245–1259. https://doi.org/10.1016/j.ipm.2019.02.018

    Article  Google Scholar 

  14. Badjatiya P, Gupta S, Gupta M, Varma V (2017) Deep learning for hate speech detection in tweets. In: Proceedings of the 26th international conference on world wide web companion (WWW’17 Companion). International world wide web conferences steering committee, Republic and Canton of Geneva, Switzerland, pp 759–760. https://doi.org/10.1145/3041021.3054223

  15. Del Vigna F, Cimino A, Dell’Orletta F, Petrocchi M, Tesconi M (2017) Hate me, hate me not: hate speech detection on Facebook. In: Proceedings of the first Italian conference on cybersecurity, pp 86–95

    Google Scholar 

  16. Mohammad SM, Turney PD (2013) Crowdsourcing a word–emotion association lexicon. Comput Intell 29(3):436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x

    Article  MathSciNet  Google Scholar 

  17. Mohammad S, Turney P (2010) 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, pp 26–34

    Google Scholar 

  18. Mohammad SM, Kiritchenko S (2015) Using hashtags to capture fine emotion categories from tweets. Comput Intell 31(2):301–326. https://doi.org/10.1111/j.1467-8640.2012.00460.x

    Article  MathSciNet  Google Scholar 

  19. Mohammad S (2012) Emotional tweets. In: SEM 2012: 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 (SemEval 2012), pp 246–255

    Google Scholar 

  20. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau RJ (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on language in social media (LSM 2011), pp 30–38

    Google Scholar 

  21. Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012) A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 system demonstrations, pp 115–120

    Google Scholar 

  22. Gautam G, Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: 2014 Seventh international conference on contemporary computing (IC3), pp 437–442. IEEE. https://doi.org/10.1109/IC3.2014.6897213

  23. Kharde V, Sonawane P (2016) Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971. https://doi.org/10.5120/ijca2016908625

  24. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307. https://doi.org/10.5120/ijca2016908625

    Article  Google Scholar 

  25. Kouloumpis E, Wilson T, Moore J (2011) Twitter sentiment analysis: the good the bad and the omg!. In: Fifth international AAAI conference on weblogs and social media

    Google Scholar 

  26. Hailong Z, Wenyan G, Bo J (2014) Machine learning and lexicon based methods for sentiment classification: a survey. In: 2014 11th web information system and application conference, pp 262–265. IEEE. https://doi.org/10.1109/WISA.2014.55

  27. Nielsen FA (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. CoRR. https://arxiv.org/abs/1103.2903

  28. Mohammad S, Bravo-Marquez F, Salameh M, Kiritchenko S (2018) Semeval-2018 Task 1: affect in tweets. In: Proceedings of international workshop on semantic evaluation (SemEval-2018), New Orleans, LA, USA.https://doi.org/10.18653/v1/S18-1001

  29. Hutto CJ, Gilbert E (2014) VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: ICWSM 2014, pp 216–255. https://ojs.aaai.org/index.php/ICWSM/article/view/14550

  30. Balabantaray RC, Mohammad M, Sharma N (2012) Multi-class twitter emotion classification: a new approach. Int J Appl Inf Syst 4(1):48–53. https://doi.org/10.5120/ijais12-450651

    Article  Google Scholar 

  31. Zhang Z, Robinson D, Tepper J (2018) Hate speech detection using a convolution-LSTM based deep neural network. In: ESWC 2018: the semantic web

    Google Scholar 

  32. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci.https://doi.org/10.1155/2018/7068349

  33. Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 6645–6649. IEEE. https://doi.org/10.1109/ICASSP.2013.6638947

  34. Kollias D, Marandianos G, Raouzaiou A, Stafylopatis A (2015) Interweaving deep learning and semantic techniques for emotion analysis in human-machine interaction. In: 2015 10th International workshop on semantic and social media adaptation and personalization (SMAP), Trento, pp 1–6.https://doi.org/10.1109/SMAP.2015.7370086

  35. Ng HW, Nguyen VD, Vonikakis V, Winkler S (2015) Deep learning for emotion recognition on small datasets using transfer learning. In: Proceedings of the 2015 ACM on international conference on multimodal interaction, pp 443–449. https://doi.org/10.1145/2818346.2830593

  36. Dos Santos C, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 69–78

    Google Scholar 

  37. Zhang M, Zhang Y, Vo D-T (2016) Gated neural networks for targeted sentiment analysis. Proc AAAI Conf Artif Intell 30(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/10380

  38. Jabreel M, Moreno A (2018) EiTAKA at SemEval-2018 Task 1: An ensemble of n-channels ConvNet and XGboost regressors for emotion analysis of tweets. arXiv preprint arXiv:1802.09233

  39. Li I, Li Y, Li T, Alvarez-Napagao S, Garcia D (2020) What are we depressed about when we talk about Covid-19: mental health analysis on tweets using natural language processing. arXiv preprint. https://arxiv.org/abs/2004.10899

  40. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

    Google Scholar 

  41. Javed N, Muralidhara BL (2015) Automating corpora generation with semantic cleaning and tagging of tweets for multi-dimensional social media analytics. Int J Comput Appl 127(12):11–16

    Google Scholar 

  42. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  43. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45(4):427–437. https://doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

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Acknowledgements

This paper and the experiments conducted, makes use of the NRC Hashtag Lexicon, created by Saif M. Mohammad at the “National Research Council Canada”. http://saifmohammad.com/WebPages/lexicons.html.

Author information

Authors and Affiliations

  1. St. Francis College, Bengaluru, India

    Nazura Javed

  2. Bangalore University, Bengaluru, India

    B. L. Muralidhara

Authors
  1. Nazura Javed
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  2. B. L. Muralidhara
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Corresponding author

Correspondence to Nazura Javed .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vinit Kumar Gunjan

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Javed, N., Muralidhara, B.L. (2022). Emotions During Covid-19: LSTM Models for Emotion Detection in Tweets. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Lecture Notes in Networks and Systems, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-6407-6_13

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