Partial Attention Modeling for Sentiment Analysis of Big Data

Sentiment analysis has grown in importance as a component of NLP. Sentiment analysis in service assessment can help leaders modify programs correctly and in a timely manner to reflect users' true feelings and sentiment about the service. This will raise the standard of teaching and learning. To address the inefficiencies and heavy workload of assessment methods in all areas, a Partial Attention (PA) model that integrates global and local attention through gated unit control is proposed. The model yields reasonable contextual representations and achieves improved classification results. According to the experimental results, the PA model outperforms current methods for applications in education and other fields.