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Role of Artificial Neural Networks Techniques in Development of Market Intelligence: A Study of Sentiment Analysis of eWOM of a Women’s Clothing Company

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School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
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Business School, Wuchang University of Technology, Wuhan 430223, China
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Department of Computer Sciences and Information Technology, University of Sargodha, Sargodha 40100, Pakistan
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Business School, Huanggang Normal University, Huanggang 438000, China
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Authors to whom correspondence should be addressed.
Academic Editor: Mirjana Pejić Bach
J. Theor. Appl. Electron. Commer. Res. 2021, 16(5), 1862-1876; https://doi.org/10.3390/jtaer16050104
Received: 27 April 2021 / Revised: 4 June 2021 / Accepted: 5 June 2021 / Published: 8 June 2021
(This article belongs to the Section Digital Marketing and the Connected Consumer)
Web 2.0 technology enables customers to share electronic word of mouth (eWOM) about their experiences. eWOM offers great market insights to the organization, and important for organization’s success. eWOM monitoring and management is one of the major contemporary challenges for the organization, because of high volume and frequency of the content. It is nearly impossible for an organization to manually monitor content generated by each user. In this paper, we propose sentiment analysis as an alternative method for analysis of emotions and behavioral intentions in real-time data. Sentiment analysis is performed on women’s e-clothing reviews collected from the Kaggle data repository. The dataset consists of 23,486 reviews, comprising ten feature variables. This study applied artificial neural network techniques to determine polarity of the data in terms of positive or negative. Sentiment analysis was performed by using two artificial neural networks, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), to classify the review as recommended (positive) or not recommended (negative). The proposed models have been evaluated on these performance measuring parameters: accuracy, recall, specificity, F1-score and roc-curve. The LSTM method outperformed CNN and achieved classification accuracy of 91.69%, specificity 92.81%, sensitivity 76.95%, and 56.67% F1-score. Based on results of this study, LSTM technique is highly recommended for the sentiment analysis of unstructured text-based user-generated content. View Full-Text
Keywords: sentiment analysis; neural networks; market intelligence; LSTM; CNN sentiment analysis; neural networks; market intelligence; LSTM; CNN
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MDPI and ACS Style

Nawaz, Z.; Zhao, C.; Nawaz, F.; Safeer, A.A.; Irshad, W. Role of Artificial Neural Networks Techniques in Development of Market Intelligence: A Study of Sentiment Analysis of eWOM of a Women’s Clothing Company. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1862-1876. https://doi.org/10.3390/jtaer16050104

AMA Style

Nawaz Z, Zhao C, Nawaz F, Safeer AA, Irshad W. Role of Artificial Neural Networks Techniques in Development of Market Intelligence: A Study of Sentiment Analysis of eWOM of a Women’s Clothing Company. Journal of Theoretical and Applied Electronic Commerce Research. 2021; 16(5):1862-1876. https://doi.org/10.3390/jtaer16050104

Chicago/Turabian Style

Nawaz, Zubair; Zhao, Chenhui; Nawaz, Fouzia; Safeer, Asif A.; Irshad, Waseem. 2021. "Role of Artificial Neural Networks Techniques in Development of Market Intelligence: A Study of Sentiment Analysis of eWOM of a Women’s Clothing Company" J. Theor. Appl. Electron. Commer. Res. 16, no. 5: 1862-1876. https://doi.org/10.3390/jtaer16050104

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