The purpose of this study was to examine word clustering in detecting Twitter trending topics about new products based on specific sentiment or interest expressions. Thus, we collected Twitter entries about new products based on specific sentiment or interest expressions. Twitter is an online social networking and microblog service that enables its users to post and read text-based messages of up to 140 characters, known as tweets. Twitter has spread rapidly in Japan in recent years. To identify market trends, analysis of consumer tweet data has received much attention recently. It is important to consider time series variation of trending topics when we perform word clustering to detect trending topics on Twitter. Personal concerns will be influenced by new product strategies, such as marketing communication strategies, and will change as time passes. In the present study, we sought to detect time series variation in topics about new products by classifying words into clusters based on the co-occurrence of words in Twitter entries. Then, we classified the words extracted from the tweet data using non-negative matrix factorization for dimensionality reduction of the vector space model.
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