Study of Network Public Opinion Classification Method Based on Naive Bayesian Algorithm in Hadoop Environment

In the light of the excellent distributed storage and parallel processing feature of hadoop cluster, a new kind of network public opinion classification method based on Naive Bayes algorithm in hadoop environment is studied. The collected public opinion documents are stored locally according to the HDFS architecture, and whose character words are extracted paralleled in Mapreduce process. Thus the naive Bayesian classification algorithm is parallel encapsulated on cloud computing platform. The MapReduce packaged Naive Bayesian classification algorithm performance is verified and the results show that the algorithm execution speed are significantly improved compared to a single server. Its public opinion classification accuracy rate is more than 85%, which can effectively improve the classification performance of network public opinion and classification efficiency.

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Study of a New Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Environment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.1624 ◽

2014 ◽

Vol 635-637 ◽

pp. 1624-1627

Author(s):

Jian Xu ◽

Bin Ma

Keyword(s):

Public Opinion ◽

Parallel Processing ◽

Nearest Neighbor ◽

Distributed Storage ◽

Classification Algorithm ◽

Classification Method ◽

Hadoop Platform ◽

Network Public Opinion

A new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop environment is studied in this paper. In the light of distributed storage and parallel processing Characteristics of Hadoop platform, the parallel K_NN classification algorithm in the frame of MapReduce is designed. The classification ability and execution efficiency of proposed scheme is verified and the results show that the parallel K_NN algorithm enhances the network public opinion classification precision and execution efficiently.

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Differential Classification Method in Different Teaching Models of Accounting Courses Based on Naive Bayesian Classification Algorithm

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v14i08.10395 ◽

2019 ◽

Vol 14 (08) ◽

pp. 38

Author(s):

Xiuying Ou

Keyword(s):

Bayesian Classification ◽

Classification Algorithm ◽

Classification Method ◽

Self Awareness ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Accounting Courses ◽

Generative Teaching ◽

The Difference ◽

Student Classification

Accounting is an important management discipline with strong theoretical foundation and practical operation. Due to the differences between individuals in the process of learning, the mastery of the subject is different. This requires teachers to implement differential teaching from the differences in student personality in the process of teaching. However, when teachers use the concept of difference teaching to teach, the classification of students' differences is mostly calculated by manual quantification such as records, tests, surveys, etc. This kind of measurement and qualitative method not only wastes manpower, but also has personal subjectivity, blindly relies on individual subjective judgment to judge students' advantages and interests, and has accuracy and scientificity. This requires research on students' differential classification methods. Therefore, this paper proposes a student classification method based on naive Bayesian algorithm. It constructs a classifier based on historical data, and then uses a well-structured and stable classifier to classify the actual pre-classification objects, and actually applies it to the teaching of accounting courses, realizing the difference in the teaching process. Provide data support for future differential teaching research. The results show that the naive Bayesian classification algorithm can be used to analyze the difference in personality and learning of students. Presupposition and generative teaching objectivesand students improve their self-awareness to better promote self-development.

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