81 papers with code • 10 benchmarks • 6 datasets
Aspect-based sentiment analysis is the task of identifying fine-grained opinion polarity towards a specific aspect associated with a given target.
Aspect Based Sentiment Analysis, PyTorch Implementations.
In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory.
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a 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).
Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.