A Layered Approach for Summarization and Context Learning from Microblogging Data

Twitter, a microblogging online social network, is one of the most popular information sharing and communication platform. The large user-base and users mutual interactions generate massive amount of data that are rich source of information for predictive modeling, sentiment analysis, opinion mining, and other text information processing tasks. Understanding context embedded within text corpus and generating a contextual summary of the corpus is one of the promising research directions in the field of data analytics. In this paper, we present a layered graph-based approach using both content and structural data to analyze and summarize tweets at different levels of granularity. The proposed approach models tweets as a multi-dimensional graph and applies random walk to identify most informative tweets, which are further processed using a graph-theoretic approach, LexRank, to identify most informative sentences for summary generation. Finally, the summary texts are analyzed using TextRank algorithm to identify prominent keywords conceptualizing the context of the underlying corpus. The proposed summary generation and context learning approach is evaluated over four different real-world Twitter datasets using standard information retrieval metrics.