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Title: Clustering Arabic tweets for sentiment analysis
Authors: Abuaiadah, Diab 
Rajendran, Dileep 
Jarrar, Mustafa 
Keywords: Electronic data processing - Distributed processing - Arabic language;Linguistic analysis (Linguistics) - Arabic language;Collocation (Linguistics), Arabic;Algorithms Machine learning, Arabic;Cluster analysis - Computer programs;Social media - Arab countries
Issue Date: 2018
Publisher: Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Abstract: The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. 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. These results are of importance as it is contrary to normal sized documents where, in many information retrieval applications, light stemming performs better than root based stemming and the Cosine function is commonly used.
DOI: 10.1109/AICCSA.2017.162
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