An empirical study on learning based methods for user consumption intention classification

Abstract

Recently, huge amount of text with user consumption intentions have been published on the social media platform, such as Twitter and Weibo, and classifying the intentions of users has great values for both scientific research and commercial applications. User consumption analysis in social media concerns about the text content representation and intention classification, whose solutions mainly focus on the traditional machine learning and the emerging deep learning techniques. In this paper, we conduct a comprehensive empirical study on the user intension classification problem with learning based techniques using different text representation methods. We compare different machine learning, deep learning methods and various combinations of them in tweet text presentation and users’ consumption intention classification. The experimental results show that LSTM models with pre-trained word vector representation can achieve the best classification performance.