Multi-task learning model based on multi-scale CNN and LSTM for sentiment classification
N Jin, J Wu, X Ma, K Yan, Y Mo - IEEE Access, 2020 - ieeexplore.ieee.org
Sentiment classification is an interesting and crucial research topic in the field of natural
language processing (NLP). Data-driven methods, including machine learning and deep …
language processing (NLP). Data-driven methods, including machine learning and deep …
A novel capsule based hybrid neural network for sentiment classification
Y Du, X Zhao, M He, W Guo - IEEE Access, 2019 - ieeexplore.ieee.org
Sentiment classification of short text is a challenging task because of limited contextual
information. We propose a capsule-based hybrid neural network model which can obtain the …
information. We propose a capsule-based hybrid neural network model which can obtain the …
Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification
F Liu, J Zheng, L Zheng, C Chen - Neurocomputing, 2020 - Elsevier
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 …
their ability of feature extraction. However, it remains as an enormous challenge to model …
Three-way enhanced convolutional neural networks for sentence-level sentiment classification
Y Zhang, Z Zhang, D Miao, J Wang - Information Sciences, 2019 - Elsevier
Deep neural network models have achieved remarkable results in sentiment classification.
Traditional feature-based methods perform slightly worse than deep learning methods in …
Traditional feature-based methods perform slightly worse than deep learning methods in …
Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification
W Li, F Qi, M Tang, Z Yu - Neurocomputing, 2020 - Elsevier
There are a lot of linguistic knowledge and sentiment resources nowadays, but in the current
research with deep learning framework, these kinds of unique sentiment information are not …
research with deep learning framework, these kinds of unique sentiment information are not …
Sentiment analysis with ensemble hybrid deep learning model
The rapid development of mobile technologies has made social media a vital platform for
people to express their feelings and opinions. Understanding the public opinions can be …
people to express their feelings and opinions. Understanding the public opinions can be …
An efficient sentiment analysis methodology based on long short-term memory networks
Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality
or negativity) of the text. As online markets have become more popular over the past …
or negativity) of the text. As online markets have become more popular over the past …
Sentiment and context-aware hybrid DNN with attention for text sentiment classification
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 …
data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of …
Attention-emotion-enhanced convolutional LSTM for sentiment analysis
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 …
widely used in sentiment representation learning and detection of texts. However, most of …
Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data
Abstract Analysis of consumer reviews posted on social media is found to be essential for
several business applications. Consumer reviews posted in social media are increasing at …
several business applications. Consumer reviews posted in social media are increasing at …