Extract Aspect

7 papers with code • 1 benchmarks • 0 datasets

Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.

Most implemented papers

A Multi-task Learning Framework for Opinion Triplet Extraction

GeneZC/OTE-MTL Findings of the Association for Computational Linguistics 2020

The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms.

An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis

tshi04/DMSC_FEDA 18 Sep 2020

In addition, we also propose an Attention-driven Keywords Ranking (AKR) method, which can automatically discover aspect keywords and aspect-level opinion keywords from the review corpus based on the attention weights.

Extractive Opinion Summarization in Quantized Transformer Spaces

stangelid/qt 8 Dec 2020

We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.

IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis

senticnet/IARM EMNLP 2018

Sentiment analysis has immense implications in e-commerce through user feedback mining.

Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

xuuuluuu/Aspect-Sentiment-Classification EMNLP 2020

Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.

Aspect-Sentiment-Multiple-Opinion Triplet Extraction

l294265421/asmote 14 Oct 2021

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i. e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment.