# Will sentiment analysis need subculture? A new data augmentation approach

arXiv: https://arxiv.org/abs/2309.00178 · HTML: https://mlsanity.com/paper/2309.00178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation](https://mlsanity.com/paper/2009.13818/) (2020)
- [Generative Data Augmentation for Commonsense Reasoning](https://mlsanity.com/paper/2004.11546/) (2020)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2103.15912/) (2021)
- [Data augmentation for low resource sentiment analysis using generative adversarial networks](https://mlsanity.com/paper/1902.06818/) (2019)
- [A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process](https://mlsanity.com/paper/2111.13854/) (2021)
- [A New Multifractal-based Deep Learning Model for Text Mining](https://mlsanity.com/paper/2111.13861/) (2021)
- [A Zipf's Law-based Text Generation Approach for Addressing Imbalance in Entity Extraction](https://mlsanity.com/paper/2205.12636/) (2022)
- [10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches](https://mlsanity.com/paper/1711.07915/) (2017)

## Built on by (most cited first, 1 of 1)

- [LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods](https://mlsanity.com/paper/2407.00322/) (2024)

## Abstract (arXiv)

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.