Comparison Performance of Long Short-Term Memory and Convolution Neural Network Variants on Online Learning Tweet Sentiment Analysis

2021 ◽  
pp. 3-17
Author(s):  
Muhammad Syamil Ali ◽  
Marina Yusoff
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

scholarly journals An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism

2021 ◽  
Vol 4 (4) ◽  
pp. 85
Author(s):  
Hashem Saleh Sharaf Al-deen ◽  
Zhiwen Zeng ◽  
Raeed Al-sabri ◽  
Arash Hekmat
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Arbitrary Length ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Improved Model

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text's actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews.

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