Combination of Domain Knowledge and Deep Learning for Sentiment Analysis

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.

arXiv commentAccepted to MIWAI 2017

References in corpus (3)

• Thumbs up? Sentiment Classification using Machine Learning Techniques
• Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
• Text Understanding from Scratch

Cited by in corpus (4)

• Applications and Techniques for Fast Machine Learning in Science
• Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
• Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
• Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media