Consumer reviews analysis on cycling pants in online shopping malls using text mining
Source abstract
Abstract
Abstract This study was investigated trends and consumer awareness on cycling pants by analyzing the reviews on bib shorts, bib tights, shorts, and tights in online shopping malls using text mining. The reviews and product information on cycling pants from Jan. 2017 to the first half of 2020 were crawled, and a total of 7241 cases were analyzed. The keywords of cycling pants were extracted using a Korean morphological analyzer (KoNLP), calculated to the term-document matrix, and then converted into a co-occurrence matrix. The number of reviews of cycling pants increased by 39% per year, and especially in the first half of 2020, the number of reviews has doubled over compared to the first half of last year. Bib shorts accounted for more than 50% of the number of reviews of cycling pants and received the highest rating, making them the most preferred. Positive reviews on cycling pants appeared 15 times over than that of negative reviews, and most of the cycling pants were evaluated positively. Size and cost-effective appeared as the important keywords both in positive and negative reviews. However, it was found that consumers have a difficult time choosing the size not only in the negative but also in the positive reviews. Pad was the keyword that appeared the most in negative reviews, and it was the most dissatisfied factor in the cycling pants. Therefore, in an internet shopping mall, it is necessary to provide intuitive and accurate information that is easy for consumers to understand about information on the size and pad of the cycle pants.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Diverse Topics in Contemporary Research · Consumer Perception and Purchasing Behavior · Technology and Data Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
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