Feb, 2017
Life Aspect Inference of Tweets Based on Probability Distribution.
Web Intelligence and Agent Systems: An International Journal
- ,
- ,
- Volume
- 15
- Number
- 1
- First page
- 55
- Last page
- 65
- Language
- English
- Publishing type
- Research paper (scientific journal)
- DOI
- 10.3233/WEB-170352
- Publisher
- IOS Press
Many people share their daily events and opinions on Twitter. Some tweets are beneficial and others are related to such aspects of a user's real-life as eating, traffic conditions, and weather. In this paper, we propose an inference method of the real-life aspect distribution of tweets using labeled tweets. Our method infers the aspect probability distributions by a hierarchical estimation framework (HEF), which is hierarchically composed of both unsupervised and supervised machine learning methods. In the first phase, it extracts topics from a sea of tweets using Latent Dirichlet Allocation (LDA). In the second phase, it builds associations between topics and real-life aspects using a small set of labeled tweets. The probability distribution of aspects is inferred using the associations based on the bag of terms extracted from unknown tweets. Our sophisticated experimental evaluations with a large amount of actual tweets demonstrate the high efficiency and robustness of our inference method. Especially in the case of single-label training, HEF showed significantly lower JSD values than other baseline methods, such as Naive Bayes, SVM, and L-LDA.
- Link information
- ID information
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- DOI : 10.3233/WEB-170352
- ISSN : 2405-6464
- ISSN : 2405-6456
- SCOPUS ID : 85013421797