Research on the Application Algorithm of Natural Language Processing Technology in Text Analysis

First, this study constructed a text encoding strategy based on Word2Vec, which laid the data foundation for subsequent classification and sentiment interpretation by converting the corpus into the vector field. Then, this paper introduced multiple text classification technologies including support vector machine (SVM) and convolutional neural network (CNN), combined with various feature extraction methods to achieve text classification. This study further optimized the sentiment analysis algorithm, focusing on the accurate identification of positive and negative emotions, and adopted the collaborative operation of sentiment tendency vocabulary and deep learning framework to improve the accuracy of sentiment identification. This paper selected multiple sets of publicly available data sets to perform experiments to evaluate the performance of various algorithms. The experimental results show that the text classification scheme based on Word2Vec and CNN has achieved more than $85 \%$ accuracy, precision and recall, and the accuracy of sentiment analysis is as high as $\mathbf{8 7 . 6 \%}$. The above data strongly proves that the algorithm designed in this study shows excellent performance in processing text analysis tasks. Through detailed data analysis, this study confirms the feasibility and practical application value of natural language processing technology in text classification and sentiment analysis scenarios.