LAIM: Life Aspect Inference Method Based on Probability Distribution for Real Life Tweets
Shuhei Yamamoto, Noriko Kando, Tetsuji Satoh · 2015
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, weather, and so on. In this paper, we propose an inference method of the real life aspect distribution of tweets using a 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.