A weighted metric scalarization approach for multiobjective BOHB hyperparameter optimization in LSTM model for sentiment analysis

Viadinugroho, Raden Aurelius Andhika; Rosadi, Dedi

doi:10.1016/j.ins.2023.119282

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Therefore, CNNs have begun to be widely used in sentiment analysis [17][18][19]. The variant model of the recurrent neural network (RNN) is also a common method used in sentiment analysis [20]. For instance, in the case of using aspect word information, Tang et al [21] introduced target-dependent long short-term memory (TD-LSTM).…”

Section: Related Workmentioning

confidence: 99%

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

Gao,

Jiang,

Zou

et al. 2024

Applied Sciences

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Aspect-level sentiment analysis is a research focal point for natural language comprehension. An attention mechanism is a very important approach for aspect-level sentiment analysis, but it only fuses sentences from a semantic perspective and ignores grammatical information in the sentences. Graph convolutional networks (GCNs) are a better method for processing syntactic information; however, they still face problems in effectively combining semantic and syntactic information. This paper presents a sentiment-supported graph convolutional network (SSGCN). This SSGCN first obtains the semantic information of the text through aspect-aware attention and self-attention; then, a grammar mask matrix and a GCN are applied to preliminarily combine semantic information with grammatical information. Afterward, the processing of these information features is divided into three steps. To begin with, features related to the semantics and grammatical features of aspect words are extracted. The second step obtains the enhanced features of the semantic and grammatical information through sentiment support words. Finally, it concatenates the two features, thus enhancing the effectiveness of the attention mechanism formed from the combination of semantic and grammatical information. The experimental results show that compared with benchmark models, the SSGCN had an improved accuracy of 6.33–0.5%. In macro F1 evaluation, its improvement range was 11.68–0.5%.

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Section: Related Workmentioning

confidence: 99%

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

Gao,

Jiang,

Zou

et al. 2024

Applied Sciences

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“…It prunes the hyperparameter search space by applying the successive halving method [50] using the values obtained from various early terminations performed multiple times. BOHB [7] combines the sampling strategy of the treestructured Parzen estimator [51] with the pruning strategy of hyperband [52]. In multiple studies, BOHB outperformed Bayesian optimization and hyperband in high-dimensional and diverse models.…”

Section: Bayesian Optimization Hyperband (Bohb) For Multi-step Foreca...mentioning

confidence: 99%

Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models

Yang,

Kim,

Ryu

et al. 2024

Electronics

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In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers.

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Continuous dynamic gesture recognition using surface EMG signals based on blockchain-enabled internet of medical things

Bai

Jiang

et al. 2023

Information Sciences

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A weighted metric scalarization approach for multiobjective BOHB hyperparameter optimization in LSTM model for sentiment analysis

Cited by 5 publications

References 32 publications

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models

Continuous dynamic gesture recognition using surface EMG signals based on blockchain-enabled internet of medical things

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