Sentiment Analysis With Comparison Enhanced Deep Neural Network

Abstract

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 existing results show that models based on RNN and CNN have good performance. In order to improve the performance of text sentiment analysis, we reformulate the classification task as a comparing problem, and propose Comparison Enhanced Bi-LSTM with Multi-Head Attention (CE-B-MHA). In fact, it is efficient to classify by comparison mechanism instead of doing complex calculation. In this model, bidirectional LSTM is used for initial feature extraction, and valuable information is extracted from different dimensions and representation subspaces by Multi-Head Attention. The comparison mechanism aims to score the feature vectors by comparing with the labeled vectors. The experimental results show that CE-B-MHA has better performance than many existing models on three sentiment analysis datasets. INDEX TERMS Sentiment analysis, machine learning, neural networks.

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

“…33 and Fig. 34, we observe that our proposal based on the deep learning model (CNN+FFNN), Hadoop framework, and Mamdani fuzzy system outperforms the other used approaches (Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36]) with accuracy equal to 99.83%,99.99%, and execution time equal to 0.0089s, and 0.00534 on Sentiment140 dataset and COVID-19_Sentiments dataset respectively. Our proposal's significant effectiveness and performance are due to the application classifier, we did another experiment that compares our proposal with the other selected approaches from the literature (Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36]).…”

“…The size of our FDLC parameters is equal to (21.76M,47.82M) for COVID-19_Sentiments and Sen-timent140 dataset, respectively. As the experimental result shown, our proposed FDLC requires much lower space computational complexity compared to Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36] approaches. Table 15 shows the empirical results obtained after measuring the time computational complexity of our FDLC model and other chosen models in terms of training time consumption and testing time consumption.…”

“…Its objective is to capture and detect more linguistic features, thus employ them for the sentiment analysis task 89%. A "Comparison Enhanced Bi-LSTM with MultiHead Attention (CE-B-MHA)"developed by Lin et al [32]. This paper, called CE-B-MHA, combines multi-Head attention to extracting global features and the strength of Bi-LSTM to discover the local sequence features.…”

“…the process searches the rule with the minimum antecedent block that is considered to be the truth value of the fuzzy IF-THEN rule. This operation is expressed using the following equation (32):…”

“…In this last experiment, we have evaluated the effectiveness of our proposed FDLC in terms of complexity, convergence, and stability. This experiment serves to compare our proposed FDLC, Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36] and to discover the more efficient method among all evaluated approaches in terms of complexity, convergence, and stability.…”