Hybrid shark and bear smell optimization algorithm with deep neural network for sentiment analysis.

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Abstract

Nowadays, Twitter is a well-liked media platform system where people can publish any browser material. Since the trends found will be so helpful to telemedicine in a specific way, this openly accessible user information is indeed essential. These are some of the uses is the computerized detection of psychological issues, such as anxiety. Earlier research on accurately detecting depressed individuals on social sites has mostly depended upon consumer behaviors, language skills, and interpersonal relationships. The drawback is that those same systems are developed on even a variety of unrelated data that may not be essential for identifying a depressed individual. Additionally, the performance and efficiency of the system as a whole are negatively impacted by these contents. Designers suggest a unique multimodal concept, it’s a combination of recurrent neural network and attention-based residual network categorization unit with decision-level fusion algorithms that address the limitations of the current artificial depressive detection techniques. The Hybrid Shark and Bear Smell Optimization Algorithm extract features from audio, video, and text.

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Keywords

Bear smell optimization algorithm; Classification; Deep learning; Deep neural network; Depression; Feature extraction; Fusion; Sentiment Analysis; Shark optimization algorithm; social media; Twitter.