• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.189-199
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• 2013

The goal of this study was to contemplate the impact of emotional elements of motion graphics on the brand preference in websites of TV products. To attain the goal, the emotional elements of motion graphics in websites of TV products, i.e., color, graphic image, typography, and layout, were set as independent variables and the brand preference as a dependent variable. The variables were analyzed objectively. Samples were collected from selected design students attending technical colleges. Among 282 samples collected, 15 were discarded as unfeasible and the remaining 267 were used in the analysis. Statistical analysis techniques used in the study included factor analysis, reliability analysis, correlation analysis, and multiple regression analysis; and 'SPSS Win. 11.5' was used to perform the statistical analysis. From the analysis, the following two results were obtained. First, it appeared that emotional elements of motion graphics appeared in websites of TV products exerted statistically significant impacts on the brand preference. Second, the element exerting the most significant impact on the brand preference among the emotional elements were appeared as 'graphic image' and 'color'. Thus, it was concluded that it is necessary to give priority in 'graphic image' and 'color' to enhance the brand preference.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.195-207
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• 2021

In recent years, with the rapid growth of China's animation industry, the two-dimensional culture and consumption have been immersed in the daily lives of young people. The two-dimensional culture that mainly exists on young people is gradually entering the public's field of vision, making the two-dimensional culture not It is only restricted to the fixed fan circle, but is known to more people outside the circle. At the same time, the "barrage" (screen text) cultures in video websites has become popular with some film and television works, Internet terms, etc., and has attracted the attention on mainstream culture. On the one hand, its cultural products have appeared on traditional mainstream video websites and advertisements on provincial satellite TV. And in the program, on the other hand, a small part of the screen text and cultural terms are also used by some celebrities and other ordinary people who don't understand the meaning of the terms at all, and have caused widespread dissemination. Sometimes the video website itself is also mentioned, which obviously shows a difference. The tendency towards a kind of screen texts subculture to penetrate the mainstream culture.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.