The goal of aspect-based sentiment analysis (ABSA) is to identify the polarity specific aspects in a context. Recently, graph neural networks have employed dependent tree syntactic information assess link between and contextual words; nevertheless, most this research has neglected phrases that are insensitive effect various sentence. In paper, we propose dual-channel edge-featured attention mod...

Abstract Mining opinions from reviews has been a field of ever-growing research. These include mining on document level, sentence level and even aspect level. While explicitly mentioned aspects user-generated texts have widely researched, very little work done in gathering that are implied not mentioned. Previous to identify implicit opinion was limited syntactic-based classifiers or other mach...

Today people, exchanging their thoughts through online web forums, blogs, and different platforms for social media. In shopping, they are giving reviews opinions on other various products, brands, services. Their towards a product do not only purchase decisions of the consumers but also improves quality about requirements find out product's particular problem get an excellent solution that prod...

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word prediction. However, such a suffers from one drawback: only few frequent words polarities tended be taken into consideration for final decision while abundant infrequent ignored by models. To deal this issue, we propose progressive self-supervi...

Aspect-based sentiment analysis (ABSA) is a crucial fine-grained task that aims to determine polarity in specific aspect term. Recent research has advanced prediction accuracy by pre-training models on ABSA tasks. However, due the lack of data, those cannot be trained effectively. In this paper, we propose cross-domain generative data augmentation framework (CDGDA) utilizes generation model pro...