Abstract
Web content is consumed at anytime and anywhere through mobile devices. Consumption behavior has been affected by its own emotional content. Web content has been categorized by article’s topic and its emotion has been determined by article’s nuance. This study is to determine category and emotion of web content. The Automatic Content Categorization System (ACCS) has been developed to crawl the texts from web page and to separate texts into morpheme using natural language processing (NLP). Finally, web content was classified into category and emotion by document similarity. The main contribution of this study is to provide fixed categories and 28 emotions to classify web content for analyzing consumption behavior of web content.
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Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2015-0-00312, The development of technology for social life logging based on analyzing social emotion and intelligence of convergence contents).
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Jo, Y., Kim, H., Lee, H., Whang, M. (2018). Classification of Web Content by Category Generation in Social Life Logging. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_216
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DOI: https://doi.org/10.1007/978-981-10-7605-3_216
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