Aspect-Based Sentiment Analysis (ABSA)

156 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 )


Use these libraries to find Aspect-Based Sentiment Analysis (ABSA) models and implementations

Most implemented papers

Effective LSTMs for Target-Dependent Sentiment Classification

songyouwei/ABSA-PyTorch COLING 2016

Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence.

Aspect Level Sentiment Classification with Deep Memory Network

songyouwei/ABSA-PyTorch EMNLP 2016

Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory.

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence


Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA).

Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

xuuuluuu/SemEval-Triplet-data 5 Nov 2019

In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

yangheng95/LCF-ATEPC 17 Dec 2019

Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC).

Interactive Attention Networks for Aspect-Level Sentiment Classification

songyouwei/ABSA-PyTorch 4 Sep 2017

In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.

Attentional Encoder Network for Targeted Sentiment Classification

songyouwei/ABSA-PyTorch 25 Feb 2019

Most of the previous approaches model context and target words with RNN and attention.

Adversarial Training for Aspect-Based Sentiment Analysis with BERT

IMPLabUniPr/Adversarial-Training-for-ABSA 30 Jan 2020

In this work, we apply adversarial training, which was put forward by Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis.

An Unsupervised Neural Attention Model for Aspect Extraction

ruidan/Unsupervised-Aspect-Extraction ACL 2017

Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space.