Wong, T.C., Chan, H. K. and Lacka, E. (2017) An ANN-based approach of interpreting user-generated comments from social media. Applied Soft Computing, 52, pp. 1169-1180. (doi: 10.1016/j.asoc.2016.09.011)
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Abstract
The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper proposes an ANN-based approach to analyse user-generated comments from social media. The first mechanism of the approach is to map comments against predefined product attributes. The second mechanism is to generate input-output models which are used to statistically address the significant relationship between attributes and comment length. The last mechanism employs Artificial Neural Networks to formulate such a relationship, and determine the constitution of rich comments. The application of proposed approach is demonstrated with a case study, which reveals the effectiveness of the proposed approach for assessing product performance. Recommendations are provided and direction for future studies in social media data mining is marked.
Item Type: | Articles |
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Additional Information: | This research was partially supported by the Ningbo Soft Science Programme, the Ningbo Science and Technology Bureau (reference number: 2016A10037). |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Lacka, Dr Ewelina |
Authors: | Wong, T.C., Chan, H. K., and Lacka, E. |
College/School: | College of Social Sciences > Adam Smith Business School > Management |
Journal Name: | Applied Soft Computing |
Publisher: | Elsevier |
ISSN: | 1568-4946 |
ISSN (Online): | 1568-4946 |
Published Online: | 23 September 2016 |
Copyright Holders: | Copyright © 2016 Elsevier B.V. |
First Published: | First published in Applied Soft Computing 52:1169-1180 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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