People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and illformed data becomes challenging. To address these challenges, events are extracted from streaming social media data in the form of keyphrases using different cognitive properties. The motivation behind this research work is to provide substantial improvements in the lexical variation of event phrases while detecting events and sub-events from twitter data. In this research work, the approach towards event detection from social media data is divided into three phases namely: Identifying sub-graphs in Microblog Word Co-occurrence Network (WCN) which provides important information about keyphrases; Identifying multiple events from social media data; and Ranking contextual information of event phrases.
翻译:在社交媒体平台(如Twitter、Facebook、Pinterest和Google+)上,人们张贴关于不同主题的信息,这些主题在社交媒体平台(如Twitter、Facebook、Pinterest和Google+)上活跃词汇中。他们相互跟踪,更有可能得到更好的回应。发布当前事件信息的人会得到更好的回应。对社交媒体平台上大量数据进行手工分析是困难的。这为自动分析用户贡献的社会媒体文件打开了新的研究方向。自动社交媒体数据分析由于用户共享大量信息而变得困难。许多研究人员利用Twitter数据进行社交媒体分析(SMA),因为Twitter数据可在公共领域自由提供。从社交媒体数据中发现的事件数据大多用于不同的应用程序,例如交通拥堵检测、灾难和紧急情况管理以及现场新闻探测等。在推特平台上共享的信息性质是短、噪音和模糊的。因此,从用户生成的、不完善的数据中的事件探测和提取事件词句变得困难。为了应对这些挑战,利用不同的认知特性从社交媒体网站流式数据流数据中提取事件。本研究工作背后的动机是大幅改进社交媒体数据流数据序列,即从调查事件中的数据序列中的数据序列,从社交数据序列中进行。