Aspect-Based Sentiment Analysis (ABSA)
166 papers with code • 18 benchmarks • 18 datasets
Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing task that aims to identify and extract the sentiment of specific aspects or components of a product or service. ABSA typically involves a multi-step process that begins with identifying the aspects or features of the product or service that are being discussed in the text. This is followed by sentiment analysis, where the sentiment polarity (positive, negative, or neutral) is assigned to each aspect based on the context of the sentence or document. Finally, the results are aggregated to provide an overall sentiment for each aspect.
And recent works propose more challenging ABSA tasks to predict sentiment triplets or quadruplets (Chen et al., 2022), the most influential of which are ASTE (Peng et al., 2020; Zhai et al., 2022), TASD (Wan et al., 2020), ASQP (Zhang et al., 2021a) and ACOS with an emphasis on the implicit aspects or opinions (Cai et al., 2020a).
( Source: MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction )
Benchmarks
These leaderboards are used to track progress in Aspect-Based Sentiment Analysis (ABSA)
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Libraries
Use these libraries to find Aspect-Based Sentiment Analysis (ABSA) models and implementationsDatasets
SemEval-2014 Task-4
MAMS
Twitter Sentiment Analysis
Subtasks
Latest papers with no code
Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction
Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions.
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis
In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA).
All in One: An Empirical Study of GPT for Few-Shot Aspect-Based Sentiment Anlaysis
In this study, we used GPTs for all sub-tasks of few-shot ABSA while defining a general learning paradigm for this application.
A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning
The approach focuses on generating weakly-supervised annotations by exploiting the strengths of both large language models (LLM) and traditional syntactic dependencies.
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA.
Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis
Further, we show that the proposed methods can be extended with multiple adaptations and demonstrate a qualitative analysis of the proposed approach using sample text for aspect term extraction.
Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses
Our approach aims to overcome the inherent noise and variability in these responses, enabling a comprehensive analysis of sentiments that can support employee lifecycle management.
CERM: Context-aware Literature-based Discovery via Sentiment Analysis
Driven by the abundance of biomedical publications, we introduce a sentiment analysis task to understand food-health relationship.
Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments
Sentiment analysis methods are rapidly being adopted by the field of Urban Design and Planning, for the crowdsourced evaluation of urban environments.
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations
And we propose an ABSA-specific augmentation method to create such augmentations.
