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A Morphological Analysis Method of Predicting Place-Event Performance by Online News Titles
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A Morphological Analysis Method of Predicting Place-Event Performance by Online News Titles
Choi, Sukjae; Lee, Jaewoong; Kwon, Ohbyung;
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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.
Text Mining;Unstructured Data;Place Marketing;Place Event;Expected Demand;Morphological Analysis;
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텍스트 분석을 활용한 정보의 수요 공급 기반 뉴스 가치 평가 방안,이동훈;최호창;김남규;

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