Will sentiment analysis need subculture? A new data augmentation approach
This addresses the data scarcity issue in sentiment analysis for researchers and practitioners, but it appears incremental as it applies a new augmentation method to an existing domain.
This paper tackled the problem of insufficient training data in sentiment analysis by proposing a subculture-based data augmentation (SCDA) approach, which generated enhanced texts using subcultural expression generators, and the results showed varying degrees of sentiment stimulation from different subcultural expressions and suggested linear reversibility for some expressions.
Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture-based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions.