Combining Burst Detection for Hot Topic Extraction
Hui Fang Ma, Hui Li · Advanced materials research · 2011
As traditional text representations are not suitable for online dynamic streams, this paper presents a hot topic extraction technique that can be used for tracking news topics over time. The model combines individual word burst into the document-word vector representation, which can emphasize the temporally features of text streams. An energy ratio threshold based burst detection approach is proposed and TF-PDF is then combined to weigh the terms. Experiment results demonstrate that this model is effective in topic extraction for news stream and it can better improve the clustering performance.