Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

Deep learning has emerged as a powerful machine learning technique that learns multiple
layers of representations or features of the data and produces state‐of‐the‐art prediction …

With the increasing development of Web 2.0, such as social media and online businesses,
the need for perception of opinions, attitudes, and emotions grows rapidly. Sentiment …

Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of
data in an enormous and dynamic way. In this regard, the sentiment analysis appeared to be …

Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with
computers. Previous studies have achieved significant breakthroughs and extensive …

Many classification models work poorly on short texts due to data sparsity. To address this
issue, we propose topic memory networks for short text classification with a novel topic …

Aspect-based sentiment analysis (ABSA) is a granular-level sentiment analysis task that
aims to detect the sentiment polarities of a specified aspect in the text. This research shows …

Attention-based methods for deep neural networks constitute a technique that has attracted
increased interest in recent years. Attention mechanisms can focus on important parts of a …

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task,
which identifies the sentiment polarity of a specific aspect in a sentence. In general, the …

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of
different aspects in one sentence. Most existing approaches handle the classification by …