MessageNet: Message Classification using Natural Language Processing and Meta-data

TL;DR

MessageNet introduces a deep learning framework that combines NLP techniques with meta-data infusion to enhance message classification accuracy by leveraging multiple data channels beyond text.

Contribution

The paper presents a novel multi-modality neural network architecture that effectively integrates various message meta-data types with text for improved classification performance.

Findings

Meta-data can provide additional information not captured by text alone.

The multi-modality approach outperforms traditional text-only classifiers.

Joint training of dedicated blocks enables effective cross-channel feature learning.

Abstract

In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks, combined with a novel technique for infusing the meta-data input that is typically available in messages such as the sender information, timestamps, attached image, audio, affiliations, and more. As we demonstrate throughout the paper, going beyond the mere text by leveraging all available channels in the message, could yield an improved representation and higher classification accuracy. To achieve message representation, each type of input is processed in a dedicated block in the neural network architecture that is suitable for the data type. Such an implementation enables training all blocks together simultaneously, and forming cross channels features in the network. We show in the Experiments Section that in some cases, message's meta-data holds an additional information that cannot be extracted just from the text, and when using this information we achieve better performance. Furthermore, we demonstrate that our multi-modality block approach outperforms other approaches for injecting the meta data to the the text classifier.