Journal
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
Volume 33, Issue 4, Pages 125-141Publisher
IGI GLOBAL
DOI: 10.4018/JOEUC.20210701.oa6
Keywords
Opinion Mining; Product Development; Sentiment Analysis; Social Networking Sites
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In the world of social networking, consumers tend to rely on expert comments or product reviews before making buying decisions. However, online posts are often short and may contain both positive and negative sentiments, making it challenging to accurately determine sentiment polarity.
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.
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