Target-specific sentiment analysis method combining word-masking data enhancement and adversarial learning

Xiaoyang Liu, Shanghong Dai, Giacomo Fiumara, Pasquale De Meo

Abstract: Target-specific sentiment analysis is an emerging topic in the field of text mining but current approaches to deriving the polarity of a sentence suffer from two main drawbacks: on one hand, we lack of a large and well-curated corpus, and on the other hand, current solutions based on deep learning are particularly vulnerable to the attack of adversarial samples. A novel target-specific sentiment classification method is proposed. Firstly, the method of masking target entities is applied to replace synonyms and insert words randomly; secondly, the target-specific sentiment classification model of adversarial learning is constructed with six baseline models; finally, we combine data enhancement and adversarial learning to construct target-specific sentiment classification model. Experimental results show that Macro-F1 values are improved by 0.30–2.91, 0.88–2.42 and 0.13–1.94% compared to the six baseline models by using Laptop14, Restaurant14 and Twitter original datasets, respectively, using Adversarial learning. Using word-masking data enhancement samples and Adversarial learning from Laptop14, Restaurant14 and Twitter shows that Macro-F1 values are improved by 0.9–2.64, 1.59–3.09 and 0.18–1.71% compared to the six baseline (SC), respectively. Our method can effectively improve the quality of samples, it improves the classification performance and the capability of adversarial samples defense.