  Earthquakes have a deep impact on wide areas, and emergency rescue operations
may benefit from social media information about the scope and extent of the
disaster. Therefore, this work presents a text miningbased approach to collect
and analyze social media data for early earthquake impact analysis. First,
disasterrelated microblogs are collected from the Sina microblog based on
crawler technology. Then, after data cleaning a series of analyses are
conducted including (1) the hot words analysis, (2) the trend of the number of
microblogs, (3) the trend of public opinion sentiment, and (4) a keyword and
rule-based text classification for earthquake impact analysis. Finally, two
recent earthquakes with the same magnitude and focal depth in China are
analyzed to compare their impacts. The results show that the public opinion
trend analysis and the trend of public opinion sentiment can estimate the
earthquake's social impact at an early stage, which will be helpful to
decision-making and rescue management.
