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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Review Article

A Sentiment Analysis Based Approach for Customer Segmentation

Author(s): Anisha Bhatnagar and Madhulika Bhatia*

Volume 16, Issue 2, 2022

Published on: 22 January, 2021

Article ID: e180122190638 Pages: 11

DOI: 10.2174/1872212115666210122161605

Price: $65

Abstract

Background: Customer Segmentation is the process of dividing customers into groups based on some demographic factors in order to get an idea of the targeted audience for a product and to best market said product.

Objective: Sentiment Analysis on customer reviews is one way that this process can be enhanced to get not just demographic information but subjective information and preferences as well.

Methods: In this study, Long Short-Term Memory model, a deep learning technique, has been applied for Sentiment Analysis and its results have been used to perform Customer Segmentation on demographic data containing information such as age and gender. Segmentation was performed using Spectral Clustering. Cluster Labels were extracted to perform supervised classification using different supervised algorithms, such as Support Vector Machines, Random Forests, Decision Trees and Logistic Regression.

Results: An accuracy of 90.9% was achieved by the LSTM model. An accuracy of 100% was achieved by the Random Forest and Decision Tree Classifiers.

Keywords: Customer segmentation, sentiment analysis, long short-term memory (LSTM), spectral clustering, random forests, decision trees

Graphical Abstract

[1]
Ş. Ozan, "A Case Study on Customer Segmentation by using Machine Learning Methods", 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1-6. Malatya, Turkey
[http://dx.doi.org/10.1109/IDAP.2018.8620892]
[2]
A. Joy Christy, A. Umamakeswari, L. Priyatharsini, and A. Neyaa, "RFM ranking – An effective approach to customer segmentation", Journal of King Saud University - Computer and Information Sciences,, 2018.
[3]
Yihua, "VIP customer segmentation based on data mining in mobile- communications industry", 2010 5th International Conference on Computer Science & Education, Hefei, 2010, pp. 156-159.
[4]
H. Gong, and Q. Xia, Study on Application of Customer Segmentation Based on Data Mining Technology 2009 ETP International Conference on Future Computer and Communication,, 2009, pp. 167-170. Wuhan
[http://dx.doi.org/10.1109/FCC.2009.66]
[5]
S. Zhou, and G. Lei, Application of Data Mining Technology in Membership Supermarket’s Customer Segmentation2011 International Conference on Business Computing and Global Informatization, 2011, pp. 181-183. Shanghai
[http://dx.doi.org/10.1109/BCGIn.2011.53]
[6]
N.R. Maulina, I. Surjandari, and A.M.M. Rus, Data Mining Approach for Customer Segmentation in B2B Settings using Centroid-Based Clustering, 2019 16th International Conference on Service Systems and Service Management (ICSSSM),, 2019, pp. 1-6. Shenzhen, China
[http://dx.doi.org/10.1109/ICSSSM.2019.8887739]
[7]
X. Lai, Segmentation Study on Enterprise Customers Based on Data Mining Technology2009 First International Workshop on Data-base Technology and Applications, 2009, pp. 247-250. Wuhan Hubei
[http://dx.doi.org/10.1109/DBTA.2009.96]
[8]
W. Gao, Research on customer segmentation based on OCA cluster ensemble in customer crisis managementProceedings of Interna-tional Conference on Information Systems for Crisis Response and Management (ISCRAM), 2011, pp. 180-183. Harbin Heilongjiang
[http://dx.doi.org/10.1109/ISCRAM.2011.6184101]
[9]
W. Qadadeh, and S. Abdallah, "Customers Segmentation in the Insurance Company (TIC) Dataset", Procedia Comput. Sci., vol. 144, pp. 277-290, 2018.
[http://dx.doi.org/10.1016/j.procs.2018.10.529]
[10]
Ozan and L. O. Iheme, Artificial Neural Networks in Customer Segmentation2019 27th Signal Processing and Communications Applications Conference (SIU), 2019, pp. 1-4. Sivas, Turkey
[11]
R. Monika, S. Deivalakshmi, and B. Janet, Sentiment Analysis of US Airlines Tweets Using LSTM/RNN 2019 IEEE 9th International Conference on Advanced Computing (IACC), 2019, pp. 92-95. Tiruchirappalli, India
[http://dx.doi.org/10.1109/IACC48062.2019.8971592]
[12]
X. Bai, Text classification based on LSTM and attention2018 Thirteenth International Conference on Digital Information Management (ICDIM), 2018, pp. 29-32. Berlin Germany
[http://dx.doi.org/10.1109/ICDIM.2018.8847061]
[13]
M. Dwarampudi, and N. V. Subba Reddy, Subba Reddy, Effects of padding on LSTMs and CNNs arXiv..
[14]
E. Phaisangittisagul, An Analysis of the Regularization Between L2 and Dropout in Single Hidden Layer Neural Network 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), , 2016, pp. 174-179. Bangkok
[http://dx.doi.org/10.1109/ISMS.2016.14]
[15]
Nisha and Puneet Jai Kaur, A survey of clustering techniques and algorithms 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom),, 2015, pp. 304-307. New Delhi
[16]
K. Güzel, and O. Kurşun, un, “Improving spectral clustering using path-based connectivity”, 2015 23nd Signal Processing and Communica-tions Applications Conference (SIU)., 2015, pp. 2110-2113. Malatya
[http://dx.doi.org/10.1109/SIU.2015.7130288]
[17]
K. Li, and Y. Liu, A Spectral Clustering Algorithm Based on Self-Adaption 2007 International Conference on Machine Learning and Cybernetics, 2007, pp. 3965-3968. Hong Kong
[http://dx.doi.org/10.1109/ICMLC.2007.4370839]

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