Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning

• Autores: Surbhi Saxena, Anant Deogaonkar, Rupesh Pais, Reshma Pais
• Localización: International Journal of Professional Business Review: Int. J. Prof.Bus. Rev., ISSN 2525-3654, ISSN-e 2525-3654, Vol. 8, Nº. 4, 2023 (Ejemplar dedicado a: Continuous publication; e01852)
• Idioma: inglés
• DOI: 10.26668/businessreview/2023.v8i4.1216
• Enlaces
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• Resumen

  • 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.

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