Combination of domain knowledge and deep learning for sentiment analysis of short and informal messages on social media

Abstract

Sentiment analysis has been emerging recently as one of major natural language processing (NLP) tasks, with the increasing significance of social media channels for brands to observe user opinions about their products. In the previous work, we proposed to combine the typical deep learning techniques with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. However, there is a high volume of short and informal messages posted by users which makes the existing works suffer from many difficulties. In this work, we further improve our architecture, aiming to handle those problems, by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements help us to enjoy significant improvement in performance once experimenting on real datasets.