An Innovative Examination of Tensor Blending Network for Multi-Model Sentimental optimization Using Opinion Mining

The Tensor Blending Network (TBN) for multi-model sentimental optimization is a novel approach for fusing the outputs of multiple models in order to maximize the performance of sentiment analysis. TBN is based on an encoder-decoder architecture consisting of a convolutional neural network (CNN), a Long Short Term Memory (LSTM) RNN, and a deep-learning model for sentiment classification. TBN uses attention mechanisms to blend the output of each model, focusing on the most relevant parts to extract the desirable sentiment analysis. The framework of TBN is also designed in a way that allows for easy deployment in different contexts, as the user can fuse two predefined models or multiple individual ones. Additionally, the TBN architecture can be used as a method to improve the accuracy and precision of sentiment analysis algorithms by integrating different sources of knowledge. Finally, the performance of TBN has been tested on a number of datasets, obtaining in most cases the highest accuracy and precision, proving the advantages of using this framework.