Aspect Category Detection
10 papers with code • 4 benchmarks • 3 datasets
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews
The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning
Aspect category detection is an essential task for sentiment analysis and opinion mining.
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training
In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i. e., weakly positive indicators) for the aspects of interest.
Aspect category sentiment analysis (ACSA) aims to predict the sentiment polarities of the aspect categories discussed in sentences.
The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics.
Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments.
Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of aspect-based sentiment analysis, which aims to detect aspect categories accurately with limited training instances.
Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format.