Combination of Domain Knowledge and Deep Learning for Sentiment Analysis

TL;DR

This paper proposes a novel method combining domain knowledge with deep learning to enhance sentiment analysis accuracy, addressing the importance of sentiment terms and improving the loss function for better error reflection.

Contribution

It introduces a new approach that integrates sentiment scores and a penalty matrix into deep learning models for sentiment analysis, which is a novel contribution.

Findings

Significant improvement in classification accuracy.

Effective use of sentiment scores learned by quadratic programming.

Enhanced loss function reflecting sentiment misclassification errors.

Abstract

The emerging technique of deep learning has been widely applied in many different areas. However, when adopted in a certain specific domain, this technique should be combined with domain knowledge to improve efficiency and accuracy. In particular, when analyzing the applications of deep learning in sentiment analysis, we found that the current approaches are suffering from the following drawbacks: (i) the existing works have not paid much attention to the importance of different types of sentiment terms, which is an important concept in this area; and (ii) the loss function currently employed does not well reflect the degree of error of sentiment misclassification. To overcome such problem, we propose to combine domain knowledge with deep learning. Our proposal includes using sentiment scores, learnt by quadratic programming, to augment training data; and introducing the penalty matrix for enhancing the loss function of cross entropy. When experimented, we achieved a significant improvement in classification results.