A semantic approach based on domain knowledge for polarity shift detection using distant supervision

One of the main challenges in sentiment analysis is the polarity shift. Studies have shown that the detection of polarity shifts is very effective to improve the accuracy of sentiment analysis. However, the problem of polarity shift detection has not been well studied, and most studies have only focused on detecting negations, one kind of polarity shifts. This paper aims to provide a semantic method based on domain knowledge for the detection of polarity shifts. In the proposed method, a polarity shift-tagged corpus is created using the idea of distant supervision. Thereafter, the polarity shifts are detected by training the machine learning classifiers on the resulting corpus, based on the semantic features extracted from the domain knowledge. The experimental results reveal that the SVM classifier with training on the constructed corpus is capable of detecting the polarity shifts with 79.33% accuracy and 81.21% F-measure, which are 24.6% and 17.5% more accurate than the best-performing existing method, respectively. Also, the results show that with the use of the polarity shift tag as a feature, SVM classifier F-measure for sentiment analysis has been improved up to 1.2%.