Text sentiment analysis on E‐shopping product reviews using chaotic coyote optimized deep belief network approach

Mohana, Rajni; Rajathi, K.; Kousalya, K.; Yuvaraja, T.

doi:10.1002/cpe.7039

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Shan (2023) proposed a sentiment analysis method based on convolutional neural network (CNN)bidirectional gated recurrent unit (BiGRU), in which emotional features of different granularity are extracted through CNN, and these emotional features are input into the BiGRU network to get the text emotion type. Mohana et al's (2022) study was based on the chaos coyote optimized deep belief network (DBN) for text sentiment analysis. They used a DBN classifier to extract features from the trained data, resulting in an accurate sense of innocence (positive or negative).…”

Section: Text Level Sentiment Analysismentioning

confidence: 99%

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Yin,

Chen

2023

International Journal of Data Warehousing and Mining

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Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.

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Section: Text Level Sentiment Analysismentioning

confidence: 99%

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Yin,

Chen

2023

International Journal of Data Warehousing and Mining

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A Review of Metaheuristic Optimization Techniques in Text Classification

Kaya,

Kilimci,

Uysal

et al. 2024

IJCESEN

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Metaheuristic algorithms, inspired by natural phenomena and human-based strategies, offer versatile approaches to navigate diverse search spaces and adapt to dynamic environments. These algorithms, including evolutionary algorithms, swarm intelligence, bio-inspired methods, human-based approaches, and plant-inspired techniques, have found applications across diverse domains such as engineering, finance, healthcare, logistics, and telecommunications. In the text classification domain, metaheuristic techniques have emerged as powerful tools to enhance the accuracy, efficiency, and robustness of classification systems. By optimizing feature subsets, fine-tuning model parameters, and addressing challenges such as feature selection, dimensionality reduction, class imbalance, and noisy data, metaheuristic algorithms provide flexible solutions that adapt to various text datasets and tasks. This review paper comprehensively explores recent advancements in metaheuristic applications in text classification across six categories. From evolutionary-based methods to swarm-based approaches, bio-inspired techniques to physics/chemistry-based strategies, human-based methods to plant-based algorithms, researchers have leveraged diverse metaheuristic techniques to push the boundaries of text classification. Through a systematic analysis of recent research studies, this review provides insights into the strengths, limitations, and future directions of metaheuristic optimization in the context of text classification.

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Text sentiment analysis on E‐shopping product reviews using chaotic coyote optimized deep belief network approach

Cited by 2 publications

References 29 publications

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

A Review of Metaheuristic Optimization Techniques in Text Classification

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