Multi-classification of business types on twitter based on topic model

Abstract

Today many businesses have adopted Twitter as a new marketing channel to promote their products and services. One of the potentially useful applications is to recommend users to follow businesses which match their interests. One possible solution is to apply classification algorithm to predict user's Twitter posts into some predefined business categories. Due to the short length characteristic, classifying Twitter posts is very difficult and challenging. In this paper, we propose a feature processing framework for constructing text categorization models. A topic model is constructed from a set of terms based on the Latent Dirichlet Allocation (LDA) algorithm. We apply the topic model for two different feature processing approaches:(1) feature transformation, i.e., using a set of topics as features and (2) feature expansion, i.e., appending a set of topics to a set of terms.Experimental results show that the highest accuracy of 95.7% is obtained with feature expansion technique, an improvement of 18.7% over the Bag of Words (BO W) model.

A linguistic approach for determining the topics of Spanish Twitter messages

“…Some authors propose applying techniques for dimensionality reduction. Thongsuk et al [16] propose a method for classifying Twitter messages into three categories (Airline, Food and Computer & Technology) using Latent Dirichlet Allocation (LDA [17]) to cluster words extracted from tweets into a set of 50 topics, which are then used as features of an SVM classifier [18]. They perform some experiments on a purpose-specific corpus, reporting a significant improvement with respect to a baseline provided by a bag-of-words approach.…”