Sentiment and Semantic Deep Hierarchical Attention Neural Network for fine grained News Classification

Abstract

The purpose of this study is to examine the differences between different types of news stories. Given the huge impact of social networks, online content plays an important role in forming or changing the opinions of people. Unlike traditional journalism where only certain news organizations can publish content, online journalism has given chance even for individuals to publish. This has its own advantages like individual empowerment but has given a chance to a lot of malicious entities to spread misinformation for their own benefit. As reported by many organizations in recent history, this even has influence on major events like the outcome of elections. Therefore, it is of great importance now, to have some sort of automated classification of news stories. In this work, we propose a deep hierarchical attention neural architecture combining sentiment and semantic embeddings for more accurate fine grained classification of news stories. Experimental results show that the sentiment embedding along with semantic information outperform several state-of-the-art methods in this task.