Aspect-based sentiment analysis is the task of identifying fine-grained opinion polarity towards a specific aspect associated with a given target.
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Most of the previous approaches model context and target words with RNN and attention.
Ranked #1 on Sentiment Analysis on Twitter
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).
Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC.
Ranked #8 on Aspect-Based Sentiment Analysis on SemEval 2014 Task 4 Sub Task 2 (using extra training data)
The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.