A Composite Approach in Sentiment Analysis using Random Multimodal Deep Learning
Keywords:
Aspect Based Sentiment research, Natural language processing, Random multimodal deep computing, Meta heuristic methodAbstract
An essential job in natural language processing is aspect-centered sentiment evaluation, which entails classifying the opinion represented in a text into positive, negative, and neutral categories with regard to particular features or attributes of a given entity. While traditional sentiment analysis techniques have shown success, they often struggle with capturing the nuances and complexities present in real-world text. A unique deep neural learning method for Aspect-Based Sentiment evaluation is proposed in this research. To assign a negative, positive, or neutral sentiment polarity to every extracted aspect, a neural network model called random multimodal deep learning is used. The capability of neural networks is intended to automatically learn and capture intricate relationships between textual content and corresponding sentiments associated with various aspects. Deep learning model is trained with a hybrid approach called Dwarf Mangoose Chimp Optimization algorithm to get better classification accuracy. The proposed approach introduced an innovative hybrid strategy which combines the strength of deep learning with meta heuristic methods for training the model for sentiment analysis, resulting in improved accuracy and robustness. In comparison to current methodologies, the strategy produces competitive and outstanding results.
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