Research on Text Classification Based on Improved TF-IDF Algorithm

In solving the problem of feature weight calculation for automatic text classification, we use the most widely used TF-IDF algorithm. Although the algorithm is widely used, there is a problem that the feature categories have different weights when calculating the weights. This paper proposes an improved TF-IDF algorithm (TF-IDCRF) that takes into account the relationships between classes to complete the classification of texts. By modifying the calculation formulas of IDF to correct the problem of insufficient classification of feature categories, the naive Bayes classification algorithm is used to complete the classification. Finally, the proposed algorithm is compared with two others improved TFIDF algorithms. The results of the three text classification evaluation indicators show that the proposed algorithm has certain advantages in text classification.