• The Journal of Economics, Marketing and Management
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• v.10 no.3
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• pp.21-36
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• 2022

Purpose: The purpose of this study is to compare and analyze the enterprise's score index calculated from atypical data and corrected data. Research design, data, and methodology: In this study, news articles which are non-financial information but qualitative data were collected from 2,432 SMEs that has been extracted "square proportional stratification" out of 18,910 enterprises with fixed data and compared/analyzed each enterprise's score index through text mining analysis methodology. Result: The analysis showed that qualitative data can be quantitatively evaluated by region, industry and period by collecting news from SMEs, and that there are concerns that it could be an element of alternative credit evaluation. Conclusion: News data cannot be collected even if one of the small businesses is self-employed or small businesses has little or no news coverage. Data normalization or standardization should be considered to overcome the difference in scores due to the amount of reference. Furthermore, since keyword sentiment analysis may have different results depending on the researcher's point of view, it is also necessary to consider deep learning sentiment analysis, which is conducted by sentence.

• MALSORI
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• no.50
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• pp.71-83
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• 2004

Recently, broadcast news speech recognition has become one of the most attractive research areas. If we can transcribe automatically the broadcast news and store their contents in the text form instead of the video or audio signal itself, it will be much easier for us to search for the multimedia databases to obtain what we need. However, the desirable speech signal in the broadcast news are usually affected by the interfering signals such as the background noise and/or the music. Also, the speech of the reporter who is speaking over the telephone or with the ill-conditioned microphone is severely distorted by the channel effect. The interfered or distorted speech may be the main reason for the poor performance in the broadcast news speech recognition. In this paper, we investigated some methods to cope with the problems and we could see some performance improvements in the noisy broadcast news speech recognition.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.25-31
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• 2018

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.530-539
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• 2002

Recently, there have been a lot of researches to develop an archive system for news videos that usually has a fixed structure. However, since the meta-data representation and storing schemes for news video are different from each other in the previously proposed archive systems, it was very hard to exchange these meta-data. This paper proposes a scheme for news video based on MPEG-7 MDS that is an international standard to represent the contents of multimedia, and a summarization mechanism reflecting the characteristics of shots in the news videos. The proposed scheme for news video uses the MPEG-7 MDS schemes such as VideoSegment and TextAnnotation to keep the original structure of news video, and the proposed summarization mechanism uses a slide-show style presentation of key frames with associated audio to reduce the data size of the summary video.

• Ocean and Polar Research
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• v.35 no.4
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• pp.465-489
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• 2013

We collected references on South Pacific Studies from several resources: mainly internet resources and periodicals. Internet resources include associations, organizations and societies; cultural resources; environmental issues; government data; information resources; journals and news letters; libraries, archives, publishers; news sources; other sources; regional issues; selected full text documents and digital resources; and statistics. Periodicals include news and popular interest magazines; regional magazines and general interest publications; and scholarly and professional journals.

• Asian Journal for Public Opinion Research
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• v.9 no.1
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• pp.48-82
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• 2021

This research article attempts to understand the current situation of fake news on social media in India. The study focused on four characteristics of fake news based on four research questions: subject matter, presentation elements of fake news, debunking tool(s) or technique(s) used, and the social media site on which the fake news story was shared. A systematic sampling method was used to select a sample of 90 debunked fake news stories from two Indian fact-checking websites, Alt News and Factly, from December 2019 to February 2020. A content analysis of the four characteristics of fake news stories was carefully analyzed, classified, coded, and presented. The results show that most of the fake news stories were related to politics in India. The majority of the fake news was shared via a video with text in which narrative was changed to mislead users. For the largest number of debunked fake news stories, information from official or primary sources, such as reports, data, statements, announcements, or updates were used to debunk false claims.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.15-32
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• 2016

Online news on the Internet, as published open data, contain facts or opinions about a specific affair and hence influences considerably on the decisions of the general publics who are interested in a particular issue. Therefore, we can predict the people's choices related with the issue by analyzing a large number of related internet news. This study aims to propose a text analysis methodto predict the outcomes of events that take place in a specific place. We used topics of the news articles because the topics contains more essential text than the news articles. Moreover, when it comes to mobile environment, people tend to rely more on the news topics before clicking into the news articles. We collected the titles of news articles and divided them into the learning and evaluation data set. Morphemes are extracted and their polarity values are identified with the learning data. Then we analyzed the sensitivity of the entire articles. As a result, the prediction success rate was 70.6% and it showed a clear difference with other analytical methods to compare. Derived prediction information will be helpful in determining the expected demand of goods when preparing the event.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.10-19
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• 2003

Caption information in news videos can be useful for video indexing and retrieval since it usually suggests or implies the contents of the video very well. In this paper, a new algorithm for extracting and recognizing characters from news video is proposed, without a priori knowledge such as font type, color, size of character. In the process of text region extraction, in order to improve the recognition rate for videos with complex background at low resolution, continuous frames with identical text regions are automatically detected to compose an average frame. The image of the averaged frame is projected to horizontal and vertical direction, and we apply region filling to remove backgrounds to produce the character. Then, K-means color clustering is applied to remove remaining backgrounds to produce the final text image. In the process of character recognition, simple features such as white run and zero-one transition from the center, are extracted from unknown characters. These feature are compared with the pre-composed character feature set to recognize the characters. Experimental results tested on various news videos show that the proposed method is superior in terms of caption extraction ability and character recognition rate.

• Korean Journal of Cognitive Science
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• v.12 no.1_2
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• pp.35-42
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• 2001

In this paper. a document is represented as a weighted graph called a text relationship map. In the graph. a node represents a vector of nouns in a sentence, an edge completely connects other nodes. and a weight on the edge is a value of the similarity between two nodes. The similarity is based on the word overlap between the corresponding nodes. The importance of a node. called an aggregate similarity in this paper. is defined as the sum of weights on the links connecting it to other nodes on the map. In this paper. we present a Korean text summarization system using the aggregate similarity. To evaluate our system, we used two test collection, one collection (PAPER-InCon) consists of 100 papers in the field of computer science: the other collection (NEWS) is composed of 105 articles in the newspapers and had built by KOROlC. Under the compression rate of 20%. we achieved the recall of 46.6% (PAPER-InCon) and 30.5% (NEWS) and the precision of 76.9% (PAPER-InCon) and 42.3% (NEWS).