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Artificial protozoa lotus effect algorithm enabled cognitive brain optimal model for sentiment analysis utilizing multimodal data
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DOI：10.1016/j.csl.2025.101929.png)
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摘要
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Understanding public sentiment derived from online data is a challenging research problem with numerous applications, including contextual analysis and opinion assessment on specific events. Traditionally, sentiment analysis has concentrated on a single modality, such as text or images. However, utilizing multimodal information such as images, text, and audio can enhance model accuracy. Despite this advantage, combining visual and textual features often leads to decreased performance. This is mainly because of the model's inability to efficiently capture the intricate relationships amongst diverse modalities. To confront these challenges, a new technique named Artificial Protozoa Lotus Effect Algorithm _ Cognitive Brain Optimal Model (APLEA_CBO) model has been developed for sentiment analysis using multimodal data. Initially, feature extraction is performed on audio data to obtain the feature vector outcome-1. Similarly, feature extraction is conducted on the input text to extract suitable features and is considered outcome-2. Both feature sets are then processed for sentiment analysis using the Cognitive Brain Optimal Model (CBOM), which is developed by employing Recurrent Denoising Long Short-Term Memory (RD-LSTM). The CBOM is trained using the Artificial Protozoa Lotus Effect Algorithm (APLEA), which is the integration of Artificial Protozoa Optimization (APO) and Lotus Effect Algorithm (LEA). It is noted that the APLEA_CBO model has gained an FPR of 7.17 %, a recall of 92.76 %, a precision of 90.62 %, and an accuracy of 90.60 %.
Keyword:
Multimodal data
Sentiment analysis
Cognitive brain optimal model
Recurrent denoising long short-term memory
Artificial protozoa optimization
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3.4
论文数:
1.5K
被引数:
2.6K
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