Combination of Domain Knowledge and Deep Learning for Sentiment Analysis
By Khuong Vo, Dang Pham, Mao Nguyen, Trung Mai, Tho Quan. 2017
8 citations recorded by the source corpus.
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 regression, to augment training data; and introducing penalty matrix for enhancing the loss function of cross entropy. When experimented, we achieved a significant improvement in classification results.
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- Ugur Kursuncu
- Manas Gaur
- Amit P. Sheth
- Jérôme Euzenat
- Yannis Kalfoglou
- Bastian Quilitz
- Qinbin Li
- Srikumar Venugopal
Citations in the corpus
- Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “To overcome such limitations in a sentiment clas-sification task, (Vo et al. 2017) adds sentiment scores into the feature set for enhancing the learned representation and modifies the loss function to respond to values of the sentiment score during learning.”