Big Data Analysis for Event Detection in Microblogs

被引:6
作者
Cherichi, Soumaya [1 ]
Faiz, Rim [2 ]
机构
[1] Univ Tunis, ISG, LARODEC, Tunis, Tunisia
[2] Univ Carthage, IHEC, LARODEC, Tunis, Tunisia
来源
RECENT DEVELOPMENTS IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS | 2016年 / 642卷
关键词
Microblogs; Relevant information; NLP; Event detection; Big data;
D O I
10.1007/978-3-319-31277-4_27
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The growing complexity of the Twitter micro-blogging service in terms of size, number of users, and variety of bloggers relationships have generated a big data which requires innovative approaches in order to analyse, extract and detect non-obvious and popular events. Under such a circumstance, we aim, in this paper, to use big data analytics within twitter to allow real time event detection. These challenges present a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large-scale data-analysis applications. Taking to account all the difficulties, this paper proposes a new metric to improve the results of the searches in microblogs. It combines content relevance, tweet relevance and author relevance, and develops a Natural Language Processing method for extracting temporal information of events from posts more specifically tweets. Our approach is based on a methodology of temporal markers classes and on a contextual exploration method. To evaluate our model, we built a knowledge management system. Actually, we used a collection of 10 thousand of tweets talking about the current events in 2014 and 2015.
引用
收藏
页码:309 / 319
页数:11
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