Topic Classification for Short Texts

Abstract

In the context of TV and social media surveillance, constructing models to automate topic identification of short texts is key task. This paper formalizes the topic classification as a top-K multinomial classification problem and constructs worth-to-consider models for practical usage. We describe the full data processing pipeline, discussing about dataset selection, text preprocessing, feature extraction, model selection and learning, including hyperparameter optimization. When computing time and resources are limited, we show that a classical model like SVM performs as well as an advanced deep neural network, but with shorter model training time