An Event Detection Method Combining Temporal Dimension and Position Dimension

Abstract

Increasing numbers of tweets streaming data present both challenges and opportunities to improve event detection approach. So, it is important to propose a method that can solve some challenges. One such challenge is to automatically detect a set of events from large texts dynamically. The unique features of tweets, such as short and noisy content, diverse and fast changing topics, and large data volume, make event detection a challenge. Many previous works on event detection focused on supervised methods that did not take temporal information of the text and the position information of the words into account simultaneously. In this paper, we propose an unsupervised approach for event detection from tweets or texts that incorporates information from all positions of a word’s occurrences into a biased PageRank and the temporal information into the tweets or texts. Our proposed model obtains remarkable improvements in performance over three event detection methods called Joint Model, Globe Vector- Latent Dirichlet Allocation, and Language Independent Neural Network that do not take into account word positions and temporal information for this task. Specifically, on three datasets of tweets and texts. Our method achieves higher precision and less time cost. Our experiments on three datasets of tweets and corpora show that our proposed model obtains better results than the latest three event detection methods Joint Model, GV-LDA, LINN Through quantitative analysis and qualitative analysis in our paper, we can have a better understanding of our proposed method.