Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the text literature, many Bayesian generative models re proposed to represent documents and words in order to process text effectively and accurately. As the most popular one of these models, Latent Dirichlet Allocation Model(LDA) did great job in dimensionality reduction for document classification. , inspiring by latent Dirichlet allocation model,  propose LDCM or latent Dirichlet category model for text classification rather than dimensionality reduction. LDCM estimate parameters of models by variational inference and use variational parameters to estimate maximum a posteriori of terms. As demonstrated by our experimental results,  report satisfactory categorization performances about our method on various real-world text documents.
['Shibin Zhou', 'Kan Li', 'Yushu Liu']
I am writing a research paper on a idea Text Classification Based on a Novel Bayesian Hierarchical Model
[{"from": "human", "value": "I am writing a research paper on a idea Text Classification Based on a Novel Bayesian Hierarchical Model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the text literature, many Bayesian generative models re proposed to represent documents and words in order to process text effectively and accurately. As the most popular one of these models, Latent Dirichlet Allocation Model(LDA) did great job in dimensionality reduction for document classification. , inspiring by latent Dirichlet allocation model,  propose LDCM or latent Dirichlet category model for text classification rather than dimensionality reduction. LDCM estimate parameters of models by variational inference and use variational parameters to estimate maximum a posteriori of terms. As demonstrated by our experimental results,  report satisfactory categorization performances about our method on various real-world text documents."}]