Abstract
Microblogging currently becomes a popular communication way and detecting sentiments of microblogs has received more and more attention in recent years. In this paper, we propose a new approach to detect the sentiments of Chinese microblogs using layered features. Three layered structures in representing synonyms and highly-related words are employed as extracted features of microblogs. In the first layer, “extremely close” synonyms and highly-related words are aggregated into one set while in the second and the third layer, “very close” and “close” synonyms and highly-related words are aggregated respectively. Then in every layer, we construct a binary vector as a feature. Every dimension of a feature indicates whether there are some words in the microblog falling into that aggregated set. These three features provide perspectives from micro to macro. Three classifiers are respectively built from these three features for final prediction. Experiments demonstrate the effectiveness of our approach.
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Wang, D., Li, F. (2014). Sentiment Analysis of Chinese Microblogs Based on Layered Features. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_44
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DOI: https://doi.org/10.1007/978-3-319-12640-1_44
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