Text Classification Based on LDA and Semantic Analysis

Abstract

The quality of text features directly affects the text classification effect, in order to get the text features which have the high contribution to the text classification in class, this paper proposes a text classification method based on LDA model and category semantic similarity method. The method selects text document topic features by the LDA model and calculates the semantic similarity between these features and categories combined with the word vector model. According to the size of similarity, the weight of the text feature is obtained, and the text classification feature selection and text classification are realized. Finally, the feasibility, validity and correctness of the algorithm are verified by experiments.

Text classification method for power customer complaints based on natural language processing technology and drosophila algorithm

“…The advantage of this method is that it can automatically learn without manual intervention, while the disadvantage is that it requires a large amount of data to train the model. Machine learning based text classification methods mainly train models to automatically learn the rules of classification, thereby classifying new texts [6]. The advantage of this method is that it can learn automatically, does not require manual intervention, and can achieve good results when the data volume is sufficient.…”