Sentiment analysis to determine employee job satisfaction using machine learning techniques

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografiska uppgifter
Huvudupphovsmän:	Mouli, Nazifa, Das, Protiva, Bin Muquith, Munim, Biswas, Aurnab, Kabir Niloy, MD Dilshad
Övriga upphovsmän:	Karim, Dewan Ziaul
Materialtyp:	Lärdomsprov
Språk:	English
Publicerad:	Brac University 2023
Ämnen:	Machine learning Naive bayes K-Nearest Neighbors (KNN) Deep learning Long Short Term Memory(LSTM) Gated Recurrent Unit (GRU) Convolutional Neural Network(CNN) Tokenization Recall Cognitive learning theory (Deep learning)
Länkar:	http://hdl.handle.net/10361/19357

id	10361-19357
record_format	dspace
spelling	10361-193572024-03-13T21:01:33Z Sentiment analysis to determine employee job satisfaction using machine learning techniques Mouli, Nazifa Das, Protiva Bin Muquith, Munim Biswas, Aurnab Kabir Niloy, MD Dilshad Karim, Dewan Ziaul Ahmed, Md Faisal Department of Computer Science and Engineering, Brac University Machine learning Naive bayes K-Nearest Neighbors (KNN) Deep learning Long Short Term Memory(LSTM) Gated Recurrent Unit (GRU) Convolutional Neural Network(CNN) Tokenization Recall Machine learning Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 61-63). Over the past three years, the COVID-19 epidemic had a significant impact on the labor market. Employees have been laid off and the majority of them have changed careers. If they can collect more datasets in the future, the researchers will be able to apply fine-tuning approaches to achieve perfect accuracy and precision. Incorporating hybrid models such as optimization techniques, multi-modal models, transfer learning models, hybrid deep learning models, sentiment models, etc. also broadens the scope of this study. These models can employ a variety of learning approaches, such as deep learning or traditional machine learning, and they can use many different types of data, such as text, images, or audio. The corpus was an additional strategy for improvement. These models consider lengthier texts in addition. 10% of US workers who keep their existing jobs are dissatisfied with them. Employee happiness is mostly influenced by business culture, but there are also cer tain economic and social elements that are interconnected. To ascertain the level of employee satisfaction and associated factors, significant study has been conducted. One of the most popular channels for opinion expression is social media. People now discuss the advantages and disadvantages of their work on the US-based social media site Glassdoor. For this study, total 1,56,428 data has been collected from Glassdoor.First, the data is correctly pre-processed after collection. The under standing of employee work satisfaction is provided by user ratings. For the purpose of making future predictions, the data was divided into binary class dataset and multiclass dataset. Moreover, this data is subjected to machine learning algorithms and deep learning algorithms. The best way to reach the ultimate conclusion is to use Bi-GRU for binary class dataset which has an overall accuracy of 97% and Bert model for multiclass dataset which has an accuracy of 95%. Nazifa Mouli Protiva Das Munim Bin Muquith Aurnab Biswas MD Dilshad Kabir Niloy B. Computer Science 2023-08-08T05:43:52Z 2023-08-08T05:43:52Z 2023 2023-01 Thesis ID: 18201171 ID: 18101382 ID: 20201228 ID: 19101249 ID: 18101548 http://hdl.handle.net/10361/19357 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 63 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine learning Naive bayes K-Nearest Neighbors (KNN) Deep learning Long Short Term Memory(LSTM) Gated Recurrent Unit (GRU) Convolutional Neural Network(CNN) Tokenization Recall Machine learning Cognitive learning theory (Deep learning)
spellingShingle	Machine learning Naive bayes K-Nearest Neighbors (KNN) Deep learning Long Short Term Memory(LSTM) Gated Recurrent Unit (GRU) Convolutional Neural Network(CNN) Tokenization Recall Machine learning Cognitive learning theory (Deep learning) Mouli, Nazifa Das, Protiva Bin Muquith, Munim Biswas, Aurnab Kabir Niloy, MD Dilshad Sentiment analysis to determine employee job satisfaction using machine learning techniques
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Karim, Dewan Ziaul
author_facet	Karim, Dewan Ziaul Mouli, Nazifa Das, Protiva Bin Muquith, Munim Biswas, Aurnab Kabir Niloy, MD Dilshad
format	Thesis
author	Mouli, Nazifa Das, Protiva Bin Muquith, Munim Biswas, Aurnab Kabir Niloy, MD Dilshad
author_sort	Mouli, Nazifa
title	Sentiment analysis to determine employee job satisfaction using machine learning techniques
title_short	Sentiment analysis to determine employee job satisfaction using machine learning techniques
title_full	Sentiment analysis to determine employee job satisfaction using machine learning techniques
title_fullStr	Sentiment analysis to determine employee job satisfaction using machine learning techniques
title_full_unstemmed	Sentiment analysis to determine employee job satisfaction using machine learning techniques
title_sort	sentiment analysis to determine employee job satisfaction using machine learning techniques
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19357
work_keys_str_mv	AT moulinazifa sentimentanalysistodetermineemployeejobsatisfactionusingmachinelearningtechniques AT dasprotiva sentimentanalysistodetermineemployeejobsatisfactionusingmachinelearningtechniques AT binmuquithmunim sentimentanalysistodetermineemployeejobsatisfactionusingmachinelearningtechniques AT biswasaurnab sentimentanalysistodetermineemployeejobsatisfactionusingmachinelearningtechniques AT kabirniloymddilshad sentimentanalysistodetermineemployeejobsatisfactionusingmachinelearningtechniques
_version_	1814308982841409536

Sentiment analysis to determine employee job satisfaction using machine learning techniques

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