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Clustering Arabic Tweets for Sentiment Analysis


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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.

Item Type: Paper presented at a conference, workshop or other event, and published in the proceedings
Uncontrolled Keywords: Sentiment analysis, Arabic stemmers, Clustering algorithms, K-Means clustering algorithm, Bisect K-Means clustering algorithm
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Depositing User: Diab Abuaiadah
Date Deposited: 19 Dec 2017 22:37
Last Modified: 21 Jul 2023 04:46

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