What is it about?
We evaluated the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets.
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Why is it important?
to know which algorithm and similarity metric to use when clustering short Arabic tweets.
Perspectives
We found that the combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719.
Dr Mustafa Jarrar
Birzeit University
Read the Original
This page is a summary of: Clustering Arabic Tweets for Sentiment Analysis, October 2017, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/aiccsa.2017.162.
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