Open AccessMining Negative Comment Data of Microblog Based on Merge-AP
Author(s) -
Zhijun Chen,
Wei Jin,
Shibiao Mu
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/9723780
Subject(s) - merge (version control) , microblogging , adaptability , cluster analysis , euclidean distance , computer science , social media , data mining , merge algorithm , dbscan , algorithm , artificial intelligence , information retrieval , canopy clustering algorithm , correlation clustering , world wide web , sorting algorithm , ecology , biology , sorting
A new depiction method based on the merge-AP algorithm is proposed to effectively improve the mining accuracy of negative comment data on microblog. In this method, we first employ the AP algorithm to analyze negative comment data on microblog and calculate the similarity value and the similarity matrix of data points by Euclidean distance. Then, we introduce the distance-based merge process to solve the problem of poor clustering effect of the AP algorithm for datasets with the complex clustering structure. Finally, we compare and analyze the performance of K -means, AP, and merge-AP algorithms by collecting the actual microblog data for algorithm evaluation. The results show that the merge-AP algorithm has good adaptability.