Toward Label-Efficient Emotion and Sentiment Analysis

Zhao, Sicheng; Hong, Xiaopeng; Yang, Jufeng; Zhao, Yanyan; Ding, Guiguang

doi:10.1109/jproc.2023.3309299

Cited by 4 publications

(2 citation statements)

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“…However, it has encountered several challenges. In particular, the models that have achieved state-of-the-art performance require a large amount of labeled data, which can be quite costly (Zhao et al, 2023;Zhang et al, 2022). An alternative approach is aspect extraction and classification in cross-domain settings.…”

Section: Introductionmentioning

confidence: 99%

“…Many works address this issue; hence, a systematic review is critical so that new research can exploit existing information and compare their performance. Some secondary works synthesize publications on ABSA (Zhao et al, 2023;Zhang et al, 2022), partly focusing on cross-domains. However, to the best of our knowledge, no works have been found that systematically and specifically review this area.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Review on Aspect-Based SentimentAnalysis in Cross-Domain

Santin,

Rezende

2024

Preprint

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Aspect-level sentiment analysis is crucial for consumers and institutions, enabling them to monitor satisfaction regarding specific aspects of products and services through user reviews. Over time, various artificial intelligence techniques have been implemented with significant success. However, most of these techniques rely heavily on a substantial amount of labeled data. In this context, Cross-Domain Aspect-Based Sentiment Analysis (ABSA) emerges, leveraging data from source domains to enhance performance in the target domain. This systematic review contributes to this framework by outlining the primary solutions developed to tackle this challenge. It presents their data sources, compared methods, and the evolution of the main technologies adopted while identifying gaps that may inspire future research endeavors. A new classification of models is proposed here, considering the cross-domain approach. This fresh perspective aims to assist researchers in their quest for innovation, clarifying the context of their proposal and suggesting relevant comparisons with existing works.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%