Impact of the covid-19 pandemic on the expression of emotions in social media

• Authors: Ghosh Debabrata
• Languages of publication: EN

Abstracts

EN

In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors

Keywords

EN

Classification; COVID-19; Emotion; Emotion analysis; Naïve Bayes; Pandemic; Random Forest; Support Vector Machine

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Multiple Criteria Decision Making
• Year: 2020
• Volume: 15
• Pages: 23-35

Contributors

author

Ghosh Debabrata

• Tata Consultancy Services, Kolkata, India

References

• Ahmed S., Reyadh A., Sithil F., Shah F., Shaafi A. (2020), An Attention-based Approach to Detect Emotion from Tweets, 182-187, DOI: 10.1109/IC2IE50715.2020.9274600.
• Arora S., Mayfield E., Penstein-Rosé C., Nyberg E. (2010), Sentiment Classification Using Automatically Extracted Subgraph Features, Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Association for Computational Linguistics, Los Angeles, 131-139.
• Bandhakavi A., Wiratunga N., Massie S., Deepak P. (2017), Lexicon Generation for Emotion Detection from Text, IEEE Intelligent Systems, 32, 102-108, DOI: 10.1109/MIS.2017.22.
• Chang Y.-C., Yeh W.-C., Hsing Y.-C., Wang C.-A. (2019), Refined Distributed Emotion Vector Representation for Social Media Sentiment Analysis, PloS ONE, 14(10), 1-22.
• Ekman P. (1971), Universals and Cultural Differences in Facial Expressions of Emotion, Nebraska Symposium on Motivation, 19.
• Gupta N., Gilbert M., Di Fabbrizio G. (2012), Emotion Detection in Email Customer Care, Computational Intelligence, 29, 10-16, DOI: 10.1111/j.1467-8640.2012.00454.x.
• Gupta S., Kanchinadam T., Conathan D., Fung G. (2020), Task-Optimized Word Embeddings for Text Classification Representations, Frontiers in Applied Mathematics and Statistics, 5, 67, DOI: 10.3389/fams.2019.00067.
• Gupta U., Chatterjee A., Srikanth R., Agrawal P. (2017), A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations, https://arxiv.org/abs/1707.06996.
• Hakak N., Kirmani M., Mohd M., Ahmed M., Mohsin M. (2019), Automatic Emotion Classifier: Proceedings of ICACIE 2017, Volume 1, Progress in Advanced Computing and Intelligent Engineering, 565-572, DOI: 10.1007/978-981-13-1708-8_52.
• Hakak N., Mohd M., Kirmani M., Mohd M. (2017), Emotion Analysis: A Survey, 2017 International Conference on Computer, Communications and Electronics (Comptelix), 397-402, DOI: 10.1109/COMPTELIX.2017.8004002.
• Hanser E., McKevitt P., Lunney T., Condell J. (2010), NewsViz: Emotional Visualization of News Stories, Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 125-130.
• Keshtkar F., Inkpen D. (2010), A Corpus-based Method for Extracting Paraphrases of Emotion Terms, Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 35-44.
• Kumar A., Kawahara D., Kurohashi S. (2018), Knowledge-enriched Two-layered Attention Network for Sentiment Analysis, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 2 (Short Papers), 253-258.
• Lee S., Chen Y., Huang C.-R. (2010), A Text-driven Rule-based System for Emotion Cause Detection, Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 45-53.
• Li Y., Su H., Shen X., Li W., Cao Z., Niu S. (2017), DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset, https://arxiv.org.pdf/1710.03957.pdf.
• Mohammad S., Turney P. (2010), Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon, Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text.
• Ovesdotter Alm C., Roth D., Sproat R. (2005), Emotions from Text: Machine Learning for Text-based Emotion Prediction, Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, 579-586.
• Pearl L., Steyvers M. (2010), Identifying Emotions, Intentions, and Attitudes in Text Using a Game with a Purpose, Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Association for Computational Linguistics, 71-79.
• Potamias R., Siolas G., Stafylopatis A. (2020), A Transformer-based Approach to Irony and Sarcasm Detection, Neural Computing and Applications, 32, DOI: 10.1007/s00521-020-05102-3.
• Ramanand J., Bhavsar K., Pedanekar N. (2010), Wishful Thinking: Finding Suggestions and 'Buy' Wishes from Product Reviews, Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing, 54-61.
• Russell J.A. (2005), Emotion in Human Consciousness Is Built in Core Affect, Journal of Consciousness Studies, 12, 8-10, 26-42.
• Shaver P., Schwartz J., Kirson D., O’Connor C. (1987), Emotion Knowledge: Further Exploration of a Prototype Approach, Journal of Personality and Social Psychology, 52(6), 1061-1086.
• Strapparava C., Valitutti A. (2004), WordNet-Affect: An Affective Extension of WordNet, Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), 1083-1086.
• Volkova E., Mohler B., Meurers D., Gerdemann D., Bülthoff H., Inkpen D., Strapparava C. (2010), Emotional Perception of Fairy Tales: Achieving Agreement in Emotion Annotation of Text, Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 98-106.
• Wiebe J., Wilson T., Cardie C. (2005), Annotating Expressions of Opinions and Emotions in Language, Language Resources and Evaluation, 39(2-3), 165-210.
• (www 1) Types Emotions and Their Effect on Human Behavior, https://www.verywellmind.com/an-overview-of-the-types-of-emotions-4163976 (accessed: 5.04.2021).
• (www 2) Information collected from social networking site, www.twitter.com (accessed: 5.10.2020).
• (www 3) Qiao F., Logistic Regression Model Tuning with Scikit-learn, https://towardsdatascience.com/(accessed: 8.01.2019).
• (www 4) Dawson C., SVM Parameter Tuning, https://towardsdatascience.com/ (accessed: 31.05.2020).
• (www 5) Koehrsen W., Hyperparameter Tuning the Random Forest in Python, https://towardsdatascience.com/ (accessed: 10.01.2018)

Identifiers

ISSN

2084-1531