The Smart Analysis of Multi-Attention Based Sentimental Optimization Model for Complex Data Fusion in High Density Cloud Servers

Multi-attention based sentimental optimization model for complex data fusion is a new hybrid model for sentiment analysis of complicated datasets. It combines multiple complementary mechanisms, including a recurrent neural network (RNN) with an attention memory network and an administrative technique to organize multi-modal training data. The model can not only provide high accuracy in sentiment categorization, but also be able to learn from partially labeled training data. Firstly, the RNN helps to capture detailed and complex information from the mixtures of heterogeneous features. Also, the attention memory network helps to summarize high-level features of the data and establish correspondent relationships with other dimensions of the input data. Finally, the administrative technique helps to make use of multiple sources of knowledge to optimize the model performances. This hybrid model has the ability to capture cross-modal relations and common features of multiple datasets and allows for a more effective fusion process. In summary, the multi-attention based sentimental optimization model for complex data fusion is an effective and efficient tool for sentiment analysis.