Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning

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.

Full text

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WORKPLACE PRODUCTIVITY THROUGH EMPLOYEE SENTIMENT ANALYSIS USING
MACHINE LEARNING
Surbhi Saxena
A
, Anant Deogaonkar
B
, Rupesh Pais
C
, Reshma Pais
D
ARTICLE INFO
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.
Doi: https://doi.org/10.26668/businessreview/2023.v8i4.1216
Article history:
Received 31 January 2023
Accepted 10 April 2023
Keywords:
Sentiment;
Employee Productivity;
Machine Learning;
Pandemic.
PRODUTIVIDADE NO LOCAL DE TRABALHO ATRAVÉS DA ANÁLISE DE SENTIMENTOS DOS
FUNCIONÁRIOS USANDO O APRENDIZADO DE MÁQUINA
RESUMO
Objetivo: O objetivo deste estudo foi analisar a produtividade no local de trabalho por meio da análise de
sentimentos dos funcionários usando aprendizado de máquina.
Estrutura teórica: Já existe muita literatura publicada sobre a produtividade dos funcionários e a análise de
sentimento como uma ferramenta, mas o estudo aqui pretende abordar os problemas da produtividade dos
funcionários pós-COVID'19.
Design/metodologia/abordagem: os autores estudaram a relação entre sentimentos e produtividade no local de
trabalho pós-COVID-19. Os sentimentos foram capturados a partir de entradas de texto fornecidas por setenta e
dois entrevistados de uma empresa de consultoria de médio porte e correlacionados com as pontuações de
produtividade . Um modelo de aprendizado de máquina foi desenvolvido usando Python para calcular a pontuação
de sentimento.
A
Assistant Professor, School of Management, Graphic Era Hill University, Bhimtal, Uttarakhand.
E-mail: surbhisaxena@gehu,ac,in Orcid: https://orcid,org/0009-0007-4008-1414
B
Assistant Professor, Department of Management Technology, Shri Ramdeobaba College of Engineering and
Management, Nagpur. E-mail: deogaonkara1@rknec,edu Orcid: https://orcid,org/0000-0003-1999-0027
C
Associate Professor, Department of Management Technology, Shri Ramdeobaba College of Engineering and
Management, Nagpur. E-mail: paisrs@rknec,edu Orcid: https://orcid,org/0000-0003-0789-5110
D
Assistant Professor, Department of Humanities, Priyadarshini College of Engineering, Nagpur.
E-mail: reshmapais@gmail.com Orcid: https://orcid.org/0009-0001-4298-5747

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
Resultados: 98,6% dos entrevistados tiveram uma alta pontuação de produtividade, enquanto 88,9% mostraram
sentimentos positivos. A maioria das respostas mostrou uma correlação positiva entre sentimentos positivos e altos
níveis de produtividade.
Implicações de pesquisa, práticas e sociais: O estudo abre caminho para a identificação de um plano de ação
para melhoria da produtividade por meio da análise de sentimento.
Originalidade/Valor: Nenhum trabalho anterior sobre a produtividade dos funcionários usando análise de
sentimentos foi feito até agora.
Palavras-chave: Sentimento, Produtividade do Empregado, Machine Learning, Pandemia.
PRODUCTIVIDAD EN EL LUGAR DE TRABAJO A TRAVÉS DEL ANÁLISIS DEL SENTIMIENTO
DE LOS EMPLEADOS MEDIANTE EL APRENDIZAJE AUTOMÁTICO
RESUMEN
Propósito: El objetivo de este estudio fue analizar la productividad en el lugar de trabajo a través del análisis de
sentimientos de los empleados utilizando el aprendizaje automático.
Marco teórico: ya se ha publicado mucha literatura sobre la productividad de los empleados y el análisis de
sentimientos como herramienta, pero el estudio aquí tiene como objetivo abordar los problemas de la productividad
de los empleados después de COVID'19.
Diseño/metodología/enfoque: los autores han estudiado la relación entre los sentimientos y la productividad en
el lugar de trabajo después de la COVID-19. Los sentimientos se capturaron a partir de las entradas de texto
proporcionadas por setenta y dos encuestados de una empresa de consultoría de tamaño medio y se correlacionaron
con las puntuaciones de productividad. . Se desarrolló un modelo de aprendizaje automático utilizando Python
para calcular la puntuación de sentimiento.
Hallazgos: el 98,6 % de los encuestados obtuvo una puntuación de productividad alta, mientras que el 88,9 %
mostró sentimientos positivos. La mayoría de las respuestas mostraron una correlación positiva entre sentimientos
positivos y altos niveles de productividad.
Implicaciones de investigación, prácticas y sociales: el estudio allana el camino para la identificación del plan
de acción para mejorar la productividad a través del análisis de sentimientos.
Originalidad/Valor: Hasta ahora no se ha realizado ningún trabajo previo sobre la productividad de los empleados
mediante el análisis de sentimientos.
Palabras clave: Sentimiento, Productividad de los Empleados, Aprendizaje Automático, Pandemia.
INTRODUCTION
A pandemic seems to be a once-in-a-lifetime event that may have far-reaching
repercussions on the global economy. The COVID-19 pandemic put nations across the globe
under lockdown. Social distancing has become the new normal for humans to survive the
deadly virus (Shoesmith et al., 2021). Remote working arrangements flourished under this
new normal. With the steady increase in jobs with remote working possible, more people are
working from home [WFH] allowing them greater flexibility to manage work and family by
reducing commute time, flexibility in work hours, higher work-life balance, etc. (Gibbs et al.,
2021).
Every coin has two sides and so does WFH, while it may offer some rewards to
employees on the personal front organizations feel there is a rift in the training and hiring
process due to remote work (WSJ, 2020). In this paper, we have assessed the sentiments of

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
employees of a mid-sized consultancy firm and established its correlation with workplace
productivity. The company had to switch to the WFH model due to COVID-19 and has now
incorporated the Hybrid working model as a part of its culture, giving employees the liberty
to work from anywhere.
One of the primary concerns of organizations post-COVID-19 pandemic is improving
employee productivity. The notion that organizational success is dependent on employee
productivity is well established, thus it has become imperative for business (Hanaysha, 2016).
It can be onerous to measure productivity and compare the results due to the myriad of
approaches followed (Nollman, 2013). The time during which an employee is working
efficiently or rather ‘mentally present’ at work can be the basis for measuring employee
productivity (Sharma, 2014).
Higher productivity results in higher success rates, enhanced work culture, competitive
compensation, and greater profits (Cato & Gordon, 2009). This culminates in motivating and
inspiring the employees to spark their creativity and achieve the pinnacle of their productivity
(Obdulio, 2014). COVID-19 pandemic has enforced WFH on numerous employees. Studies
have shown a negative correlation between WFH and productivity (Farooq & Sultana, 2021).
Whereas it has been observed that, commuting distance is positively correlated to absenteeism
in employees. The shorter the commute to the work, the higher is the productivity. Also, active
mode of transport such as cycling or walking to work not only impacts employees’ health
positively but also suggests better job performance (Ma & Ye, 2019).
Various factors influence productivity, they can be environmental factors such as air,
temperature, colour, light, space, and sound or organizational norms such as democratic
leadership (Almaamari & Alaswad, 2021; Singh, 2020). Age or rather employee experience
can be a decisive factor in elevating workplace productivity (Singh, 2020), also excellent stress
management functions in the company can alleviate physical and psychological fears among
the employees which directly improves organizational effectiveness by increasing productivity
(Sulaiman & Allah Baksh, 2019).
Money is the most crucial motivator when it comes to working. It can act as a stimulant
and enhance employee productivity when combined with workplace discipline and motivation
in form of appreciations and feedbacks (Indah et al., 2020). People work better in a safe and
hazard free environment which is created by company’s safety policy and employee job
satisfaction. It is positively correlated to the employee productivity (Morgan Morgan et al.,
2021). With vast number of people opting for WFH, the office environment is missing from

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
daily work. Workplace ergonomics have been shown to be conducive to increasing employee
productivity by creating an ergonomic environment which includes seating arrangements, type
of chairs, use of glass etc (Kumar et al., 2019).
The primary objective of the Human Resources function of any organization is to keep
their employees satisfied and happy (Harlianto, J., & Rudi. ,2023).To do so, policies must be
designed which creates an environment of collaboration and generates productive outcomes
(Gaye et al., 2021). Technology adoption acts as a mediator for HR competency enhancement
(Qaralleh, S. J., Rahim, N. F. A., & Richardson, C. (2023). Evidence-based relationships
between objectives and Human Resources function can be established by utilizing predictive
analytics tools. They utilize techniques such as Data Mining and application of various
algorithms that can be used for sentiment analysis (Malisetty et al., 2017). Sentiment analysis
classifies the text data into positive, negative, and sometimes neutral sentiment based on the
different machine learning algorithms used. Supervised and unsupervised learning can be used
in order to predict the sentiment of the given text (Medhat et al., 2014). For supervised
learning methods, a pre labelled data set is used for analysis the unknown data which is a
laborious task. Whereas unsupervised learning uses a pre-defined library for analysing the text
data (R. Khan, 2021).
With myriad of research being done on work engagement (Hanaysha, 2016),
employee behaviour monitoring (Bawane et al., 2021) and employee satisfaction and its effect
on firm’s earning (Moniz & De Jong, 2014) using sentiment analysis, the authors have
attempted to correlate employee sentiments with workplace productivity.
The objective of this study was to establish a correlation between employee sentiment
and workplace productivity by comparing the sentiment score predicted by Valence Aware
Dictionary and Sentiment Reasoning (VADER) method from the text input given by
employees to their productivity score which was calculated based on their responses to the
questions asked. A positive correlation i.e., a higher productivity score corresponding to a
positive senti ment score, indicates that a positive sentiment among the workforce might be a
contributing factor for enhanced workplace productivity.
From the literature review it was evident that productivity is not a function of a single
factor. It varies with the magnitudes of the factors mentioned in the literature. The productivity
score ranges from one to five with score above and equal to three are considered productive.
Similarly, the sentiment score ranges from negative one to positive one with score above and
equal to zero are considered positive sentiments. Studying sentiments as a function of

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
productivity was novel and unexplored. Thus, the authors have proposed the following
hypotheses for the study.
A. H0 – Workplace productivity is not correlated to employee sentiments.
H1 – Workplace productivity is correlated to the employee sentiments.
B. H0 – Mean productivity score for the sample
population = 3 H1 – Mean productivity score for the
sample population > 3
C. H0 – Mean sentiment score for the sample
population = 0 H1 – Mean productivity score for the
sample population > 0
METHODOLOGY
The study entails a survey of seventy-two employees of a mid-sized consulting firm
through a structured questionnaire. The survey consisted of three sections wherein the first
section sought demographic information such as the age and sex of the respondents. The
second section consisted of sixteen statements/ questions for assessing the productivity levels
of the respondents, and the third section had three questions to which respondents had to type
a text-based answer. These text answers were input for the sentiment analysis. A survey
questionnaire was prepared using Google forms, whereas statistical analysis was performed
on Microsoft Excel. Sentiment analysis computing was done using Jupyter notebooks
(Kluyver et al., 2016), libraries used for analysis are Natural Language Toolkit (Wagner,
2010), pandas (McKinney, 2010), NumPy (Harris et al., 2020), SciKit Learn (Pedregosa
FABIANPEDREGOSA et al., 2011).
The survey was executed online via email over a span of two weeks. Respondents were
implored to give their responses to the best of their experience and knowledge.
RESULTS AND DISCUSSION
VADER (Valence Aware Dictionary and Sentiment Reasoner) (Hutto, C.J. and
Gilbert, 2014) is a lexicon and rule-based sentiment analysis tool that is specifically attuned
to sentiments expressed in social media. VADER uses a combination of sentiment lexicon
which is a list of lexical features (e.g., words) that are generally labeled according to their
semantic orientation as either positive or negative. VADER not only talks about the Positivity
and Negativity score but also tells us about how positive or negative a sentiment is (Geeks for

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
Geeks, 2022).
A negative score indicates a negative sentiment and vice-versa, zero can be assumed
to be neutral or positive. In this study, three questions had text-based input which was inputs for
the VADER sentiment analyzer. An average of three sentiment scores was taken for individual
respondents, which was assigned as the overall sentiment score.
Productivity Score Analysis
Sixteen questions were posed to the respondents which they answered on a five-point
Likert scale. The probable responses were ‘Not at All’, ‘Rarely’, ‘Sometimes’, ‘Often’, and
‘Very Often’. To calculate productivity scores, these responses were assigned a numeric value
based on the merit of the question from one to five. Five indicated that the respondent showed
high productivity on a particular question item whereas one showed otherwise. Three was
considered the midpoint for demarcation between productive and unproductive scores.
Further, an average of sixteen such scores was taken and assigned as an overall productivity
score.
All the hypothesis validation was performed on Microsoft Excel where p < 0.05 was a
minimum level of significance.
The demographic details of the population revealed a 1:2 ratio of females to male
respondents with over 76% of the population under the age of 30.
Figure 1 Gender Distribution
Source: Prepared by authors (2022)

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
Figure 2 Age Distribution
Source: Prepared by authors (2022)
Figure 3 Sentiment - Productivity Correlation
Source: Prepared by Authors (2022)
71 of the 72 i.e., 98.6% respondents scored above or equal to 3 on the productivity
score, whereas 64 i.e., 88.9% of them score above and equal to 0 on the sentiment score. The
correlation between sentiment score and productivity score shows a positive relationship. The
majority of the 8 negative sentiments in the high productivity zone can be considered as
borderline negative or neutral sentiments. Only 1 respondent scored below 3 on the
productivity scale but has a positive sentiment score, this can be considered as borderline
productive.

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Saxena, S., Deogaonkar, A., Pais, R., Pais, R. (2023)
Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning
Figure 4 Word Cloud
Source: Prepared by Authors(2022)
The above figure shows a word cloud of frequent terms used by respondents in their text
responses. The size of the words indicates their frequency of use.
CONCLUSION
In conclusion it was found that the overall sentiment scores and overall productivity
scores were positively correlated. Thus, we can safely posit that an employee with positive
work sentiments is a productive employee. Because the respondents were working in a hybrid
work model and scored high on both the sentiment and productivity metrics, we can say that
workplace is not restricted to only offices, but it entails all the places where an employee can
possibly work.
The machine learning model used for this study is not capable of detecting sarcasm.
Thus, any response which may have been sarcastic in nature would be classified falsely. Some
of the respondents did not fill the text-based answers appropriately, they were removed from
the analysis to reduce errors. The research was carried out focusing a single firm, more
comprehensive research of large firms would have added to the quality of the outcome.
The current study aims at understanding the relationship between productivity and
employee sentiments; however, emphasis could be put on understanding the underlying
factors for productivity and sentiments along with their contributions to respective terms by
conducting a factor analysis. The role of gender and age can also be studied as they can be
contributing factors as well.

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