Will Sentiment Analysis Need Subculture? A New Data Augmentation
  Approach
Zhenhua Wang Simin He Guang Xu Ming Ren
The renowned proverb that "The pen is mightier than the sword" underscores
the formidable influence wielded by text expressions in shaping sentiments.
Indeed, well-crafted written can deeply resonate within cultures, conveying
profound sentiments. 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 strives 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 six enhanced texts for each training text by leveraging the creation
of six diverse subculture expression generators. The extensive experiments
attest to the effectiveness and potential of SCDA. The results also shed light
on the phenomenon that disparate subculture expressions elicit varying degrees
of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting
the linear reversibility of certain subculture expressions. It is our fervent
aspiration that this study serves as a catalyst in fostering heightened
perceptiveness towards the tapestry of information, sentiment and culture,
thereby enriching our collective understanding.