Challenge: a portable system for weakly-supervised aspect-based sentiment extraction is presented . ABSApp is a weakly supervised aspect based sentiment analysis system .
Approach: They present a portable system for weakly-supervised aspect-based sentiment extraction . ABSApp generates domain-specific aspect and opinion lexicons based on unlabeled dataset .
Outcome: The proposed system is interpretable and user friendly and can be quickly and cost-effectively used across domains . it generates domain-specific aspect and opinion lexicons, edits them, and generates an aspect-based sentiment report . the system has been successfully used in movie review analysis and convention impact analysis .

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AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022.emnlp-main)

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Challenge: Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health.
Approach: They propose a weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data.
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Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)

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Challenge: Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text.
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Outcome: The proposed evaluation set uncovers the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by the single-answer GT sets.
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)

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Challenge: Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks.
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Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)

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Challenge: Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information.
Approach: They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor.
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A Hybrid Approach to Aspect Based Sentiment Analysis Using Transfer Learning (2024.lrec-main)

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Challenge: Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them.
Approach: They propose a hybrid approach to Aspect-Based Sentiment Analysis using transfer learning . they exploit the strengths of large language models and traditional syntactic dependencies .
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GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)

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Challenge: Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence.
Approach: They propose a GCNet that explicitly leverages global semantic information to guide context encoding.
Outcome: The proposed model outperforms state-of-the-art methods on three public datasets.
Towards Generative Aspect-Based Sentiment Analysis (2021.acl-short)

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Challenge: Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA.
Approach: They propose to tackle various ABSA tasks in a unified generative framework . they propose to use annotation-style and extraction-style modeling to enable training .
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Benchmark Creation for Aspect-Based Sentiment Analysis in Low-Resource Odia Language and Evaluation through Fine-Tuning of Multilingual Models (2025.coling-main)

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Challenge: Aspect-based sentiment analysis is underexplored in low-resource languages such as Odia . a dataset is annotated for two tasks: Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC)
Approach: They propose to use a dataset for aspect-based sentiment analysis in Odia . they use ensemble data augmentation and a fine-tuned paraphrase generation model .
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Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews (2022.lrec-1)

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Challenge: Existing systems for sentiment analysis are focused on document and sentence levels, but there are no public datasets on aspect-based sentiment analysis for Farsi.
Approach: They propose to use a manually annotated Farsi dataset to analyze the opinion polarity of reviews . they also use transfer learning to analyze aspects of the review to improve their results .
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Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories (2021.emnlp-main)

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Challenge: Aspect-based sentiment analysis (ABSA) predicts sentiment polarity for aspect term in sentences . labeled data stored at different locations and inaccessible due to privacy or legal concerns .
Approach: They propose a model with federated learning to combine labeled data across different domains . they incorporate topic memory to take data from diverse domains into consideration .
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