LAIM: Life Aspect Inference Method Based on Probability Distribution for Real Life Tweets

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.