Modeling customer review ratings through Kmeans clustering and SVM classification

In the digital era, consumers decisions are highly influenced by online reviews, making it important for businesses to understand such electronic word of mouth in order to satisfy their customers. However, online reviews by customers are rarely straightforward, often containing mixed sentiments, mak...

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Main Author: Lim, Gina Qian Ying
Other Authors: Chen Songlin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/159096
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1590962023-03-04T20:07:13Z Modeling customer review ratings through Kmeans clustering and SVM classification Lim, Gina Qian Ying Chen Songlin School of Mechanical and Aerospace Engineering Songlin@ntu.edu.sg Engineering::Mechanical engineering In the digital era, consumers decisions are highly influenced by online reviews, making it important for businesses to understand such electronic word of mouth in order to satisfy their customers. However, online reviews by customers are rarely straightforward, often containing mixed sentiments, making it hard for one to manually gather useful insights from large volumes of data. Moreover, digital texts like online reviews in its raw form is difficult for ingestion by computers to perform analysis of customer sentiments to obtain useful information and insights. Further, although the analysis of sentiments in digital texts have been widely studied, those with neutral polarity have been largely ignored, with majority focusing on the binary problem of understanding reviews with explicit positive or negative polarity only. This study proposes an integrated machine learning model of clustering and classification techniques to understand sentiments found in customer reviews with neutral polarity. Support vector machine is the classification technique being employed together with the k-means clustering technique. The post processing for result analysis uses N-grams. The results from this study show that the model is efficient in classifying reviews with mixed sentiments and analysis of clustering results allows text contents of mixed sentiments to be understood. Bachelor of Engineering (Mechanical Engineering) 2022-06-10T00:59:25Z 2022-06-10T00:59:25Z 2022 Final Year Project (FYP) Lim, G. Q. Y. (2022). Modeling customer review ratings through Kmeans clustering and SVM classification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159096 https://hdl.handle.net/10356/159096 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Lim, Gina Qian Ying
Modeling customer review ratings through Kmeans clustering and SVM classification
description In the digital era, consumers decisions are highly influenced by online reviews, making it important for businesses to understand such electronic word of mouth in order to satisfy their customers. However, online reviews by customers are rarely straightforward, often containing mixed sentiments, making it hard for one to manually gather useful insights from large volumes of data. Moreover, digital texts like online reviews in its raw form is difficult for ingestion by computers to perform analysis of customer sentiments to obtain useful information and insights. Further, although the analysis of sentiments in digital texts have been widely studied, those with neutral polarity have been largely ignored, with majority focusing on the binary problem of understanding reviews with explicit positive or negative polarity only. This study proposes an integrated machine learning model of clustering and classification techniques to understand sentiments found in customer reviews with neutral polarity. Support vector machine is the classification technique being employed together with the k-means clustering technique. The post processing for result analysis uses N-grams. The results from this study show that the model is efficient in classifying reviews with mixed sentiments and analysis of clustering results allows text contents of mixed sentiments to be understood.
author2 Chen Songlin
author_facet Chen Songlin
Lim, Gina Qian Ying
format Final Year Project
author Lim, Gina Qian Ying
author_sort Lim, Gina Qian Ying
title Modeling customer review ratings through Kmeans clustering and SVM classification
title_short Modeling customer review ratings through Kmeans clustering and SVM classification
title_full Modeling customer review ratings through Kmeans clustering and SVM classification
title_fullStr Modeling customer review ratings through Kmeans clustering and SVM classification
title_full_unstemmed Modeling customer review ratings through Kmeans clustering and SVM classification
title_sort modeling customer review ratings through kmeans clustering and svm classification
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/159096
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