| Abstract |
| As the scale of the internet grows, the amount of subjective data increases, Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary, The former sentiment classification researches have two critical problems, First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data. |
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| Key Words |
| 감성 분류, 통계적, 클라우드, 하둡, 맵리듀스 Sentiment classification, Statistical, Cloud, Hadoop, MapReduce |
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