Data Pattern Recognition of KNN Neural Network Based on Improved Neighbor Sample Selection Strategy

This paper proposes an improved K-nearest neighbor (KNN) algorithm for sentiment analysis, which aims to improve the accuracy of sentiment classification by optimizing the neighbor sample selection strategy. With the development of the Internet and social media, user-generated content has become an important data source for sentiment analysis. This paper first reviews the existing sentiment analysis methods and their limitations, and points out the potential advantages of KNN algorithm in dealing with unstructured text data. Subsequently, an improved KNN model that optimizes the classification effect is introduced. The model dynamically adjusts the weight of neighbor samples and introduces emotional feature extraction techniques. The results show that the improved KNN algorithm has significant advantages in sentiment classification and market trend prediction, and has high accuracy and robustness. Finally, this paper summarizes the research results, and discusses the potential and future research directions of the improved algorithm in other sentiment analysis applications.