Abstract
The COVID-19 pandemic has essentially transformed the way millions of people across the world live their life. As offices remained closed for months, employees expressed conflicting sentiments on the work from home culture. People worldwide now use social media platforms such as Twitter to talk about their daily lives. This study aims to gage the public’s sentiment on working from home/remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. It is essential to study these trends at this point in the pandemic as organizations should decide whether to continue remote work indefinitely or reopen offices and workspaces, depending on productivity, and employee satisfaction. Tweets posted in the live Twitter timeline is used to generate the set of data and accessed through Tweepy API. About 2 lakh tweets relevant to the remote work during the pandemic were tokenized and then passed to Naive Bayes classifier that classifies the sentiments positive, negative, neutral to every tweet. Our findings emphasize on population sentiment which is the effects of the COVID-19 pandemic, especially resulting from the work from home policy.
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References
Kumar, A., Sebastian, T.M.: Twitter sentiment classification using distant supervision. Int. J. Comput. Sci. Issues 9(4) (2012)
Kisan, H.S., Suresh, A.P., Kisan, H.P.: Collective intelligence & sentimental analysis of Twitter data by using Standford NLP libraries with software as a service. In: Proceedings of International Conference on Computational Intelligence and Computing Research
Bouazizi, M., Ohtsuki, T.: A pattern-based approach for multi-class sentiment analysis in Twitter. IEEE Access 5, 20617–20639 (2017)
Shahana, P.H., Omman, B.: Evaluation of features on sentimental analysis. Procedia Comput. Sci. 46, 1585–1592 (2015)
Nigam, P.P., Patil, D.D., Patil, Y.S.: Sentiment classification of Twitter data: a review. Int. Res. J. Eng. Technol. (IRJET), 5(7), 929–931 (2018)
Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1.12(2009) (2009)
Padmavathia, S., Ramanujam, E.: Naïve Bayes classifier for ECG abnormalities using Multivariate maximal time series, otif. Procedia Comput. Sci. 47(2015), 222–228 (2020). https://www.sciencedirect.com/topics/engineering/naive-bayesclassifier
Al-Subaihin, S.A., Al-Khalifa, S.H.: A system for sentiment analysis of colloquial Arabic using human computation. Sci. World J. 14, 1–9 (2014)
Jianqiang, Z., Xiaolin, G.: Comparison research on text pre-processing methods on Twitter sentiment analysis. IEEE Access 5, 2870–2879 (2017)
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Hegde, N.P., Sireesha, V., Gnyanee, K., Hegde, G.P. (2022). Sentiment Analysis using COVID-19 Twitter Data. In: Bhateja, V., Satapathy, S.C., Travieso-Gonzalez, C.M., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 1. Smart Innovation, Systems and Technologies, vol 282. Springer, Singapore. https://doi.org/10.1007/978-981-16-9669-5_39
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DOI: https://doi.org/10.1007/978-981-16-9669-5_39
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