A tremendous amount of unstructured data, such as comments, opinions, and other sorts of
data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of …

Text sentiment analysis is an important but challenging task. Remarkable success has been
achieved along with the wide application of deep learning methods, but deep learning …

With the popularity of the internet, the expression of emotions and methods of
communication are becoming increasingly abundant, and most of these emotions are …

Text sentiment classification is an essential research field of natural language processing.
Recently, numerous deep learning‐based methods for sentiment classification have been …

Long short-term memory (LSTM) neural networks and attention mechanism have been
widely used in sentiment representation learning and detection of texts. However, most of …

Sentiment classification is an important research task in Natural Language Processing. To
fulfill this type of classification, previous works have focused on leveraging task-specific …

The major challenge that text sentiment classification modeling faces is how to capture the
intrinsic semantic, emotional dependence information and the key part of the emotional …

Neural networks lately have achieved a great success on sentiment classification due to
their ability of feature extraction. However, it remains as an enormous challenge to model …

Long short-term memory (LSTM) or gated recurrent units (GRUs) are usually employed to
recurrently learn variable-length sentence representations with long-range dependency in …

Sentiment analysis is a significant task in Natural Language Processing. It refers to
classification based on the emotional tendency in text by extracting text features. The …