Abstract
Nowadays, many twitter users tweet their personal affairs. Some of these posts can be quite beneficial for real life, for example, Eating, Appearance, Living, Disasters, and so on. In this paper, we propose a two phase extracting method for selecting beneficial tweets. In the first phase, many topics are extracted from a sea of tweets using Latent Dirichlet Allocation (LDA). In the second phase, associations between many topics and fewer aspects is built using a small set of labeled tweets. To enhance accuracy, the weight of feature words is calculated by information gain. Our prototype system demonstrates that the proposed method can extract the aspects of each unknown tweet.
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Yamamoto, S., Satoh, T. (2013). Two Phase Extraction Method for Extracting Real Life Tweets Using LDA. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_35
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DOI: https://doi.org/10.1007/978-3-642-37401-2_35
Publisher Name: Springer, Berlin, Heidelberg
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