关键词: 微博广告, 基于密度的空间聚类, 文本过滤, 特征提取

Abstract: There is a large amount of advertising content in micro-blog space, which seriously affects user experience and related research work. Much of existing research on micro-blog process uses classification algorithm such as Support Vector Machine (SVM) and random forest algorithm. However, it is difficult to classify a large volume of data in the classification method manually. A micro-blog advertisement publisher identification method based on clustering analysis was proposed. For user dimension, a concept of core micro-blog was put forward to deal with the phenomenon that ordinary micro-blogs were posted to dilute advertising content. Then the extracted main themes of each user and corresponding micro-blog sequences could be used to calculate user characteristics as well as the text characteristics. After that, a clustering algorithm was used to cluster the features and identify the micro-blog advertisers. The experiment result shows that the precision is 93%, the recall is 97%, and the F value is 95%, which proves that the proposed method can accurately identify the micro-blog advertisement publisher under the condition that the content of the advertisement is artificially diluted. It provides theoretical support and practical methods for the recognition and cleaning work of micro-blog spam information.

Key words: micro-blog advertising, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), text filtering, feature extraction