## 1 Introduction
### Related Works  
In the field of NLP, much research has been undertaken to provide solutions for text classification.  
[PERSON] et al. [8] presented a text classifier using Naive Bayes. They applied their model to spam filtering by using preclassified emails as prior knowledge to train their model. Their model was able to detect if an email was spam or not spam. Also, [PERSON] [9] proposed a Gaussian Naive Bayes model for text classification after proving, using 20 newsgroups and WebKB datasets, that the Gaussian Naive Bayes model was better than its classical counterpart.  
[PERSON] et al. [10] presented a text classifier using the least square support vector machine (LS-SVM) on a corpus of 91,229 words from the University of Denver's Penrose Library catalog. Their proposed LS-SVM is based on a Gaussian radial basis function (GRBF) kernel, which uses the probabilistic coefficients generated by the Latent Semantic Indexing algorithm. Its performance on this corpus was over 99.99%, outperforming the performance of Naive Bayes and K-nearest neighbor.  
[PERSON] [11] proposed a text classification algorithm to improve the performance of the Naive Bayes classifier. It was applied to spam filtering on different text corpora; the results were compared to those for the classical Naive Bayes model and were shown to outperform them. The author actually proposed a new expression for word counts, which solved the problem in Naive Bayes that multiple occurrences of the same word in a document can reduce the probability of other important features which have few occurrences.  
[PERSON] et al. [12] proposed a document classification model for the Urdu language using a single-layer multisize filters convolutional neural network (SMFCNN). They compared this model with sixteen machine learning baseline models on three imbalanced datasets. Their method achieved a higher accuracy than the selected baseline models, with accuracy values of 95.4%, 91.8%, and 93.3% on medium, large, and small size datasets, respectively.  
[PERSON] et al. [13] proposed a text classification model based on the Bidirectional Encoder Representations from Transformers (BERT) model and feature fusion. A comparison with the state-of-the-art model showed that the accuracy of the proposed model outperformed those of state-of-the-art models. The model can improve the accuracy of tag prediction for text data with sequence features and obvious local features.  
[PERSON] et al. [14] proposed an attention-based recurrent neural network for text classification. The network was trained on two news classification datasets published by NLPCC2014 and Reuters, respectively. The classification results showed that the model outperformed baseline models by achieving F-values of 88.5% and 51.8% on the two datasets.  
Conventional models focus on the classification of a text corpus without considering the heterogeneity between the features, which may reduce the explainability of their results. Also, most text classification uses a large number of features, and working with a large number of features may cause conventional approaches to be less efficient because of the curse of dimensionality.  
In this study, we propose a generative model based on collaborative partial classifications as a solution to the problem of a heterogeneous text corpus and the curse of dimensionality in text classification. We performed experiments to evaluate the performance of our model on different heterogeneous text datasets and compared the outcome with results from other studies. We propose a method to explain the classification results of our proposed model.  
The rest of the paper is organized as follows: Section 2 presents the conventional and proposed approaches to text classification and the performance measures for their evaluation. Section 3 focuses on the experimental results and discussions of the proposed approach and its comparison with other models. This work is concluded in Section 4.