| Abstract |
| User-created text data are increasing rapidly caused by development of social media. In opinion mining, User`s opinions are extracted by analyzing user`s text. A primary goal of sentiment analysis as a branch of opinion mining is to extract user`s opinions from a text that is required to build a list of emotion terms. In this paper, we built a list of emotion terms to analyse a sentiment of social media using Facebook as a representative social media. We collected data from Facebook and selected a emotion terms, and measured the dimensions of valence and activation through a survey. As a result, we built a list of 267 emotion terms including the dimension of valence and activation. |
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| Key Words |
| 소셜 미디어, 정서 단어, 정서가, 활성화, Social Media, Emotion Terms, Valence, Activation |
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