Study on the Classification of Negative Sentiment Weibo Messages in the Post-disaster Situation
H. BAI, G. YU, XY. TIAN,
Journal of Digital Information Management, Vol. 14, No. 2, 2016.
DOI: 
Link: 
Abstract:
Weibo is an extensively used social network
tool in China and has become a popular platform for disaster
information management. This popular
microblogging service offers massive firsthand information
regarding the state and emotions of victims in a disaster
situation. Identifying negative sentiment messages
from the large-scale and noisy Weibo stream is a fundamental
and challenging undertaking. Therefore, based on
the characteristics of negative Weibo messages concerning
disaster events, a novel feature selection algorithm
called combined frequent pattern (FP)-growth and mutual
information theory (CFM) algorithm, was proposed to improve
the traditional machine learning approaches in this
study. The CFM algorithm mined two FPs via FP-tree,
and the mutual information between two frequent items
was calculated to determine the most frequent and tight
features for negative-sentiment Weibo messages detection.
After that, the experimental analysis was conducted
to test the proposed novel feature selection algorithm and
to explore a suitable sentiment classifier for disaster-related
Weibo messages. The analysis employed actual
disaster-related Weibo message data set, which included
2,913 negative messages and 2,913 un-negative messages.
Results demonstrate that the CFM algorithm performs
well in the feature selection process. In particular,
this algorithm exhibits the best performance in the support
vector machine classifier with 89.34% accuracy.
Therefore, the CFM algorithm is an efficient feature selection
algorithm for negative-message classification in a
post-disaster situation. This algorithm also offers a novel
method to reduce the feature dimension in other text classification
areas.