An Internet Public Opinion Hotspot Detection Algorithm Based on Single-Pass
Gesang Duoj · 2015
By considering the time interval of Internet events as well as the importance of different feature items from semi-structured Web documents in different locations, an improved single-pass text clustering algorithm called single-pass* is proposed. The advantage is that it assigns the weight value to different feature items from different locations on the Web pages, and only needs to calculate the similarity between the new document and its seed document. Experimental results show that, compared to the single-pass algorithm, the improved algorithm can reduce the missing rate, the error detection rate, and the degradation of system performance caused by computing the topic similarity of documents in new Web data stream, and improve the clustering efficiency at an average rate of 40%. The clustered Web texts can be used to analyze the Internet opinion including the topic relevant degree and the hot degree.