Due to the rapid expansion of online platforms, review-based recommender systems have
become the standard way to determine consumers' preferences for various goods. The …

Faced the vast amount of information, choosing the appropriate materials is a prerequisite
for effective self-directed learning. The recommendation algorithm is a kind of intelligent …

With the prevalence of online review websites, large-scale data promote the necessity of
focused analysis. This task aims to capture the information that is highly relevant to a specific …

Deep learning architectures based on self-attention have recently achieved and surpassed
state of the art results in the task of unsupervised aspect extraction and topic modeling …

Aspect-based sentiment rating analysis is a decisive industry application for consumers and
service providers. This research needs an efficient tool that reflects various shades of …

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user
rating based on review texts for the items. It is based on aspect extraction with neural …

In this work we investigate the impact of encoding mechanisms used in neural aspect
extraction models on the quality of the resulting aspects. We concentrate on the neural …

In recent works, topic models for aspect-based opinion mining have been extended to
automatically train sentiment priors for topic-word distributions, leading to automated …

There is a growing body of research on biomedical information extraction on social media
texts, patient narratives, and scientific abstracts. Relatively less research has focused on the …

Humans are highly subjective beings and present their opinions on products and services in
the form of reviews, which need to be analyzed to understand the consumer base. However …