Will sentiment analysis need subculture? A new data augmentation approach

TL;DR

This paper introduces a novel subculture-based data augmentation method for sentiment analysis, leveraging subcultural expressions to enrich training data and improve model performance, highlighting the influence of subcultural nuances on sentiment detection.

Contribution

It proposes the SCDA approach that generates diverse subcultural expressions to enhance sentiment analysis training data, addressing data scarcity and capturing subcultural sentiment nuances.

Findings

SCDA improves sentiment analysis accuracy

Subcultural expressions influence sentiment intensity

Potential linear reversibility of subcultural expressions

Abstract

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.