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Keyword-Based Sentiment Mining using Twitter

M. Baumgarten, M. D. Mulvenna, N. Rooney, J. Reid

Source Title: International Journal of Ambient Computing and Intelligence (IJACI)5(2)

ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781466632714|DOI: 10.4018/jaci.2013040104

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MLA

Baumgarten, M., et al. "Keyword-Based Sentiment Mining using Twitter." IJACI vol.5, no.2 2013: pp.56-69. http://doi.org/10.4018/jaci.2013040104

APA

Baumgarten, M., Mulvenna, M. D., Rooney, N., & Reid, J. (2013). Keyword-Based Sentiment Mining using Twitter. International Journal of Ambient Computing and Intelligence (IJACI), 5(2), 56-69. http://doi.org/10.4018/jaci.2013040104

Chicago

Baumgarten, M., et al. "Keyword-Based Sentiment Mining using Twitter," International Journal of Ambient Computing and Intelligence (IJACI) 5, no.2: 56-69. http://doi.org/10.4018/jaci.2013040104

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Abstract

Big Data are the new frontier for businesses and governments alike. Dealing with big data and extracting valuable and actionable knowledge from it poses one of the biggest challenges in computing and, simultaneously, provides one of the greatest opportunities for business, government and society alike. The content produced by the social media community and in particular the micro blogging community reflects one of the most opinion- and knowledge-rich, real-time accessible, expressive and diverse data sources, both in terms of content itself as well as context related knowledge such as user profiles including user relations. Harnessing the embedded knowledge and in particular the underlying opinion about certain topics and gaining a deeper understanding of the overall context will provide new opportunities in the inclusion of user opinions and preferences. This paper discusses a keyword-based classifier for short message based sentiment mining. It outlines a simple classification mechanism that has the potential to be extended to include additional sentiment dimensions. Eventually, this could provide a deeper understanding about user preferences, which in turn could actively and in almost real time influence further development activities or marketing campaigns.

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Keyword-Based Sentiment Mining using Twitter

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