Retrieving Users’ Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis

Miriam, Anschütz,; Eder, Tobias; Groh, Georg

doi:10.1109/icsc56153.2023.00008

Cited by 1 publication

(1 citation statement)

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“…One of the factors that may be hindering the evolution of these models is the lack of labeled data at the aspect level. For instance, the work of Anschütz et al (2023) searches for images and, using these images, obtains the texts that will be used for ABSA classification. However, the model does not utilize the images in the proposed sentiment prediction model, and its training uses the SemEval 2017 dataset (Rosenthal et al, 2017).…”

Section: Future Workmentioning

confidence: 99%

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: Future Workmentioning

confidence: 99%