Aspect-Based Sentiment Analysis with Adjustments to Irrelevant Sentimental-Related Features

Aspect-based sentiment analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. While the overall polarity of a sentence has been successfully predicted with neural network architectures, aspect-specific sentiment analysis still faces challenges. In this paper, we propose a novel neural network named IRP-BERT. It is based on BERT which is the newest breakthrough in natural language processing. IRP-BERT aims at improving the accuracy of predicting the right polarity of aspects in a sentence, especially the neutral ones, because some sentimental-related words such as "love" in a noun phrase which is neutrally sentimental could have a wrong impact on the polarity classification to aspects. More specifically, we design a module named Irrelevance-Removing Process (IRP) to counteract the impact from irrelevant sentimental-related features. The final prediction depends on word representations from the outputs of both IRP and BERT. Experiments on a twitter dataset show that our model outperforms both existing state-of-the-art methods and fine-tuned BERT baselines.