Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
DOI:
https://doi.org/10.26668/businessreview/2023.v8i4.1216Keywords:
Sentiment, Employee Productivity, Machine Learning, Pandemic.Abstract
Purpose: The objective of this study was to analyze workplace productivity through employee sentiment analysis using machine learning.
Theoretical framework: A lot of literature is already published on employee productivity and sentiment analysis as a tool, but the study here is intended to address the issues in employee productivity post-COVID’19.
Design/methodology/approach: The authors have studied the relationship between sentiments and workplace productivity post-COVID- 19. Sentiments were captured from the text inputs given by seventy-two survey respondents from a mid-sized consultancy firm and correlated against the productivity scores. A machine learning model was developed using Python to calculate the sentiment score.
Findings: 98.6% of the respondents had a high productivity score, whereas 88.9% showed positive sentiments. The majority of the responses showed a positive correlation between positive sentiments and high productivity levels.
Research, Practical and Social Implications: The study paves way for identification of action plan for productivity enhancement through sentiment analysis.
Originality/Value: No previous work on employee productivity using sentiment analysis is done till now.
Downloads
References
Almaamari, Q. A., & Alaswad, H. I. (2021). FACTORS INFLUENCING EMPLOYEES’
PRODUCTIVITY- LITERATURE REVIEW. Academy of Entrepreneurship Journal, 27(3), 1– 7.
Bawane, D., Bhojane, R., & Ganage, A. (2021). Employee behavior monitoring and sentiment analysis prediction using machine learning. International Research Journal of Engineering and Technology (IRJET), 8(3), 1379–1383.
Farooq, R., & Sultana, A. (2021). The potential impact of the COVID-19 pandemic on work from home and employee productivity. Measuring Business Excellence. https://doi.org/10.1108/MBE- 12-2020-0173
Gaye, B., Zhang, D., & Wulamu, A. (2021). Sentiment classification for employees reviews using regression vectorstochastic gradient descent classifier (RV-SGDC). PeerJ Computer Science, 7, 1–27. https://doi.org/10.7717/peerj-cs.712
Gibbs, M., Mengel, F., & Siemroth, C. (2021). Work from Home & Productivity: Evidence from Personnel & Analytics Data on it Professionals. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3846680
Hanaysha, J. (2016). Improving employee productivity through work engagement: Evidence from higher education sector. Management Science Letters, January, 61–70. https://doi.org/10.5267/j.msl.2015.11.006
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. In Nature (Vol. 585, Issue 7825). https://doi.org/10.1038/s41586- 020-2649-2
Hutto, C.J. and Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for. Eighth International AAAI Conference on Weblogs and Social Media, 18. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPaper/8109
Indah, G., Sukarta, P., Sagung, A. A., & Dewi, K. (2020). Effect of Work Compensation, Motivation and Discipline on Employee Productivity. American Journal of Humanities and Social Sciences Research, 4(2), 27–33. www.ajhssr.com
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K.,
Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., & Willing, C. (2016). Jupyter Notebooks—a publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas - Proceedings of the 20th International Conference on Electronic Publishing, ELPUB 2016. https://doi.org/10.3233/978- 1-61499-649-1-87
Kumar, G. R., Bezawada, S. T., Sinno, N., & Ammoun, M. (2019). The impact of ergonomics on employees’ productivity in the architectural workplaces. International Journal of Engineering and Advanced Technology, 8(5), 1122–1132. https://doi.org/10.35940/ijeat.E1157.0585C19
Ma, L., & Ye, R. (2019). Does daily commuting behavior matter to employee productivity? Journal of Transport Geography, 76, 130–141. https://doi.org/10.1016/j.jtrangeo.2019.03.008
Malisetty, S., Archana, R. V., & Vasanthi Kumari, K. (2017). Predictive analytics in HR Management. Indian Journal of Public Health Research and Development, 8(3), 115–120. https://doi.org/10.5958/0976-5506.2017.00171.1
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. https://doi.org/10.25080/majora-92bf1922-00a
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications : A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
Moniz, A., & De Jong, F. (2014). Sentiment analysis and the impact of employee satisfaction on firm earnings. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8416 LNCS, 519–527. https://doi.org/10.1007/978-3-319-06028-6_51
Morgan Morgan, O., Emu, W., Amadi, C., Okon, E., & Njama, P. (2021). The mediating effect of job satisfaction on health and safety policy management and employee productivity in manufacturing firms. In Problems and Perspectives in Management (Vol. 19, Issue 2, pp. 104– 117). LLC CPC Business Perspectives. https://doi.org/10.21511/ppm.19(2).2021.09
Nollman, M. R. (2013). Sustainability Initiatives in the Workplace and Employee Productivity. Pedregosa FABIANPEDREGOSA, F., Michel, V., Grisel OLIVIERGRISEL, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Vanderplas, J., Cournapeau, D., Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B., Grisel, O., Dubourg, V., Passos, A., Brucher, M., Perrot andÉdouardand, M., Duchesnay, andÉdouard, & Duchesnay EDOUARDDUCHESNAY, Fré. (2011). Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. In Journal of Machine Learning Research (Vol. 12). http://scikit-learn.sourceforge.net.
Shoesmith, E., Shahab, L., Kale, D., Mills, D. S., Reeve, C., Toner, P., de Assis, L. S., & Ratschen, E. (2021). The influence of human–animal interactions on mental and physical health during the first COVID-19 lockdown phase in the U.K.: A qualitative exploration. International Journal of Environmental Research and Public Health, 18(3), 1–15. https://doi.org/10.3390/ijerph18030976
Singh, A. (2020). Association between organizational norms and employee productivity in higher education. Journal of Applied Research in Higher Education, 12(2), 271–295. https://doi.org/10.1108/JARHE-01-2019-0014
Sulaiman, N., & Allah Baksh, S. (2019). Role of stress management in increasing employee productivity at workplace. International Journal of Recent Technology and Engineering, 8(2 Special Issue 4), 744–746. https://doi.org/10.35940/ijrte.B1150.0782S419
Wagner, W. (2010). Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit. Language Resources and Evaluation, 44(4). https://doi.org/10.1007/s10579-010-9124-x
Obdulio, D. L. (2014). How management can improve corporate culture in order to have an effective work environment. Trade Publication, 75(8), 14.
R. Khan, F. Rustam, K. Kanwal, A. Mehmood and G. S. Choi, "US Based COVID-19 Tweets Sentiment Analysis Using TextBlob and Supervised Machine Learning Algorithms," 2021 International Conference on Artificial Intelligence (ICAI), 2021, pp. 1-8, doi: 10.1109/ICAI52203.2021.9445207.
Sharma, M. S., & Sharma, M. V. (2014). Employee engagement to enhance productivity in current scenario. International Journal of Commerce, Business and Management, 3(4), 595-604.
WSJ (2020): “Companies Start to Think Remote Work Isn’t So Great After All,” https://www.wsj.com/articles/ companies-start-to-think-remote-work-isnt-so-great-after-all- 11595603397.
Qaralleh, S. J., Rahim, N. F. A., & Richardson, C. (2023). JOB RESOURCE AND JOB PERFORMANCE AMONG PHYSICIANS IN THE JORDANIAN HEALTH SECTOR: THE MEDIATING ROLE OF JOB SATISFACTION. [RECURSOS DE EMPREGO E DESEMPENHO NO TRABALHO ENTRE MÉDICOS NO SETOR DE SAÚDE DA JORDÂNIA: O PAPEL MEDIADOR DA SATISFAÇÃO NO TRABALHO; RECURSOS LABORALES Y RENDIMIENTO LABORAL DE LOS MÉDICOS DEL SECTOR SANITARIO JORDANO: EL PAPEL MEDIADOR DE LA SATISFACCIÓN LABORAL] International Journal of Professional Business Review, 8(1) doi:10.26668/businessreview/2023.v8i1.378
Harlianto, J., & Rudi. (2023). Promote Employee Experience for Higher Employee Performance. International Journal of Professional Business Review, 8(3), e0827. https://doi.org/10.26668/businessreview/2023.v8i3.827
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Surbhi Saxena, Anant Deogaonkar, Rupesh Pais, Reshma Pais
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 (http://creativecommons.org/licenses/by-nc/4.0/), which allows the work to be shared with recognized authorship and initial publication in this journal.
Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html