Abstract
| Language | English |
|---|---|
| Pages | 56-69 |
| Journal | International Journal of Ambient Computing and Intelligence |
| Volume | 5 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 2013 |
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Keyword-Based Sentiment Mining using Twitter. / Baumgarten, Matthias; Mulvenna, Maurice; Rooney, Niall; Reid, John.
In: International Journal of Ambient Computing and Intelligence, Vol. 5, No. 2, 04.2013, p. 56-69.Research output: Contribution to journal › Article
TY - JOUR
T1 - Keyword-Based Sentiment Mining using Twitter
AU - Baumgarten, Matthias
AU - Mulvenna, Maurice
AU - Rooney, Niall
AU - Reid, John
PY - 2013/4
Y1 - 2013/4
N2 - 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.
AB - 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.
U2 - 10.4018/jaci2013040104
DO - 10.4018/jaci2013040104
M3 - Article
VL - 5
SP - 56
EP - 69
JO - International Journal of Ambient Computing and Intelligence
T2 - International Journal of Ambient Computing and Intelligence
JF - International Journal of Ambient Computing and Intelligence
SN - 1941-6237
IS - 2
ER -