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Assessing public opinions of products through sentiment analysis: product satisfaction assessment by sentiment analysis

Ng, CY, Law, Mo Yin Kris and Ip, Andrew WH 2021, Assessing public opinions of products through sentiment analysis: product satisfaction assessment by sentiment analysis, Journal of organizational and end user computing, vol. 33, no. 4, Jul-Aug, pp. 125-141, doi: 10.4018/JOEUC.20210701.oa6.

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Title Assessing public opinions of products through sentiment analysis: product satisfaction assessment by sentiment analysis
Author(s) Ng, CY
Law, Mo Yin KrisORCID iD for Law, Mo Yin Kris orcid.org/0000-0003-3659-0033
Ip, Andrew WH
Journal name Journal of organizational and end user computing
Volume number 33
Issue number 4
Season Jul-Aug
Start page 125
End page 141
Total pages 17
Publisher IGI Global
Place of publication Hershey, Pa.
Publication date 2021-07
ISSN 1546-2234
1546-5012
Keyword(s) Opinion Mining
Product Development
Sentiment Analysis
Social Networking Sites
Summary In the world of social networking, consumers tend to refer to expert comments or product reviews before making buying decisions. There is much useful information available on many social networking sites for consumers to make product comparisons. Sentiment analysis is considered appropriate for summarising the opinions. However, the sentences posted online are generally short, which sometimes contains both positive and negative word in the same post. Thus, it may not be sufficient to determine the sentiment polarity of a post by merely counting the number of sentiment words, summing up or averaging the associated scores of sentiment words. In this paper, an unsupervised learning technique, k-means, in conjunction with sentiment analysis, is proposed for assessing public opinions. The proposed approach offers the product designers a tool to promptly determine the critical design criteria for new product planning in the process of new product development by evaluating the user-generated content. The case implementation proves the applicability of the proposed approach.
Language eng
DOI 10.4018/JOEUC.20210701.oa6
Indigenous content off
Field of Research 0806 Information Systems
1503 Business and Management
1702 Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152273

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.