A Method for Building Burst-Annotated Co-Occurrence Networks for Analysing Trends in Textual Data
This paper presents a method for constructing a specific type of language resources that are conveniently applicable to analysis of trending topics in time-annotated textual data. More specifically, the method consists of building a co-occurrence network from the on-line content (such as New York Times articles) that conform to key words selected by users (e.g., {}Arab Spring{'}). Within the network, burstiness of the particular nodes (key words) and edges (co-occurrence relations) is computed. A service deployed on the network then facilitates exploration of the underlying text in order to identify trending topics. Using the graph structure of the network, one can assess also a broader context of the trending events. To limit the information overload of users, we filter the edges and nodes displayed by their burstiness scores to show only the presumably more important ones. The paper gives details on the proposed method, including a step-by-step walk through with plenty of real data examples. We report on a specific application of our method to the topic of Arab Spring{'} and make the language resource applied therein publicly available for experimentation. Last but not least, we outline a methodology of an ongoing evaluation of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesSimilar Papers 제목 키워드 기반
TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation
Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. In a traditional approach, the …
Time SeriesTime Series AnalysisDetection of intensity bursts using Hawkes processes: an application to high frequency financial data
Given a stationary point process, an intensity burst is defined as a short time period during which the number of counts is larger than the typical count rate. It might signal a local non-stationarity or the presence of …
Model SelectionA statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extraction
A term in a corpus is said to be ``bursty'' (or overdispersed) when its occurrences are concentrated in few out of many documents. In this paper, we propose Residual Inverse Collection Frequency (RICF), a statistical sig…
Language ModellingI/O Burst Prediction for HPC Clusters using Darshan Logs
Understanding cluster-wide I/O patterns of large-scale HPC clusters is essential to minimize the occurrence and impact of I/O interference. Yet, most previous work in this area focused on monitoring and predicting task a…
SchedulingNever Abandon Minorities: Exhaustive Extraction of Bursty Phrases on Microblogs Using Set Cover Problem
We propose a language-independent data-driven method to exhaustively extract bursty phrases of arbitrary forms (e.g., phrases other than simple noun phrases) from microblogs. The burst (i.e., the rapid increase of the oc…