Toward Text Data Augmentation for Sentiment Analysis

Abonizio, Hugo Queiroz; Paraíso, Emerson Cabrera; Barbon, Sylvio

doi:10.1109/tai.2021.3114390

Cited by 30 publications

(9 citation statements)

References 66 publications

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“…Despite the recency of text data augmentation methods in sentiment analysis, they present promising solutions to mitigate data scarcity (Abonizio et al, 2021). These ways execute class‐preserving operations on the primary data source and primarily rely on strategies such as lexical substitution (Wei & Zou, 2019; Xiang et al, 2021), word embedding interpolation (Jin et al, 2023), and neural model generation (Gupta, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…Instead, unique tasks dictate the introduction of bespoke enhancement methodologies. This could span the spectrum from commonsense reasoning and automatic translation to text comprehension and generation, extending to entity extraction and sentiment analysis (Abonizio et al, 2021; Hsu et al, 2021; Liesting et al, 2021; Liu et al, 2021; Shen et al, 2020; Xiang et al, 2021; Yang et al, 2020). Regarding sentiment analysis, popular augmentation strategies encompass synonym substitution (Zhang et al, 2015) and a technique known as easy data augmentation (EDA) (Wei & Zou, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Will sentiment analysis need subculture? A new data augmentation approach

Wang,

He,

et al. 2024

Asso for Info Science & Tech

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Will sentiment analysis need subculture? A new data augmentation approach

Wang,

He,

et al. 2024

Asso for Info Science & Tech

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“…The advancement of NLP led to a paradigm shift in text augmentation, with the introduction of various techniques such as synonym replacement, random operations, and back-translation ( Wei & Zou, 2019 ; Abonizio, Paraiso & Barbon, 2022 ; Feng et al, 2022 ; Karimi, Rossi & Prati, 2021 ). While these methods have shown effectiveness, their application is often constrained in low-resource languages, where tools akin to WordNet Miller (1995) for tasks like synonym or hyponym replacement are scarce or non-existent.…”

Section: Related Workmentioning

confidence: 99%

Text data augmentation and pre-trained Language Model for enhancing text classification of low-resource languages

Ziyaden,

Yelenov,

Hajiyev

et al. 2024

PeerJ Computer Science

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Background In the domain of natural language processing (NLP), the development and success of advanced language models are predominantly anchored in the richness of available linguistic resources. Languages such as Azerbaijani, which is classified as a low-resource, often face challenges arising from limited labeled datasets, consequently hindering effective model training. Methodology The primary objective of this study was to enhance the effectiveness and generalization capabilities of news text classification models using text augmentation techniques. In this study, we solve the problem of working with low-resource languages using translations using the Facebook mBart50 model, as well as the Google Translate API and a combination of mBart50 and Google Translate thus expanding the capabilities when working with text. Results The experimental outcomes reveal a promising uptick in classification performance when models are trained on the augmented dataset compared with their counterparts using the original data. This investigation underscores the immense potential of combined data augmentation strategies to bolster the NLP capabilities of underrepresented languages. As a result of our research, we have published our labeled text classification dataset and pre-trained RoBERTa model for the Azerbaijani language.

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“…Abonizio et al, (2022) [10] a sentiment analysis framework using NLP. The presented system considers back translation, pre-trained text augmented using a random forest algorithm.…”

Section: Background Studymentioning

confidence: 99%

Effective preprocessing and feature analysis on Twitter data for Fake news detection using RWS algorithm

Santhoshkumar,

Divya

2024

ICST Transactions on Scalable Information Systems

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The tremendous headway of web empowered gadgets develops the clients dependably strong in virtual redirection affiliations. Individuals from social affairs getting moment notices with respect to news, amusement, training, business, and different themes. The development of artificial intelligence-based classification models plays an optimum role in making deeper analysis of text data. The massive growth of text-based communication impacts the social decisions also. People rely on news and updates coming over in social media and networking groups. Micro blogs such as tweeter, facebooks manipulate the news as faster as possible. The quality of classification of fake news and real news depends on the processing steps. The proposed articles focused on deriving a significant method for pre-processing the dataset and feature extraction of the unique data. Dataset is considered as the input data for analyzing the presence of fake news. The extraction of unique features from the data is implemented using Bags of relevant tags (BORT) extraction and Bags of relevant meta words (BORMW).

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Toward Text Data Augmentation for Sentiment Analysis

Cited by 30 publications

References 66 publications

Will sentiment analysis need subculture? A new data augmentation approach

Will sentiment analysis need subculture? A new data augmentation approach

Text data augmentation and pre-trained Language Model for enhancing text classification of low-resource languages

Effective preprocessing and feature analysis on Twitter data for Fake news detection using RWS algorithm

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