• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.33-49
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• 2018

The importance of convergence activities among business is increasing due to the necessity of designing and developing new products to satisfy various customers' needs. In particular, decision makers such as CEOs are required to participate in networks between entrepreneurs for being connected with valuable convergence partners. Moreover, it is important for entrepreneurs not only to make a large number of network connections, but also to understand the networking relationship with entrepreneurs with similar topic information. However, there is a difficult limit in collecting the topic information that can show the lack of current status of business and the technology and characteristics of entrepreneur in industry sector. In this paper, we solve these problems through the topic extraction method and analyze the business network in three aspects. Specifically, there are C, S, T-Layer models, and each model analyzes amount of entrepreneurs relationship, network centrality, and topic similarity. As a result of experiments using real data, entrepreneur need to activate network by connecting high centrality entrepreneur when the corporate relationship is low. In addition, we confirmed through experiments that there is a need to activate the topic-based network when topic similarity is low between entrepreneurs.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• The Mathematical Education
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• v.61 no.4
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• pp.559-579
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• 2022

Research on lexicon and language provides insights into the interests, values and practices of a community where individuals use the language. The International Classroom Lexicon Project, in which ten countries participated, identified own country's mathematics teaching and learning lexicons by investigating mathematics classroom instruction from teachers' perspectives in a speaking-oriented community. This study, as an extension of the International Classroom Lexicon Project research, investigated pedagogical lexicons used in 「Mathematics and Education」 journals specialized for Korean professional mathematics teachers published by the Korean Society of Teachers of Mathematics. Using the text mining approach, we also traced how these pedegogical lexicons have changed quantitatively over the past 10 years with a diachronic perspective. As a results, several novel terms were found in the writing-oriented community, which were not identified in the speaking-oriented community. In addition, we could discover some pedagogical lexicons have increased statistically significantly and some lexicons appeared(increased) rapidly across years. This implies the teacher community's values and zeitgeist by reflecting these changes in the sociocultural, incidental and social changing (i.e., periodical change) contexts. This study has value as a first step in understanding zeitgeist for mathematics education in Korean mathematics teacher community according to changes of times over the past 10 years. Also, this study contributes to the methodological insights: the text mining technique provides a methodological contribution to researching changes in interests, values and zeitgeist according to these changes in the times.