Clustering Arabic Tweets for Sentiment Analysis

Abuaiadah, Diab and Dileep, Rajendran and Mustafa, Jarrar (2017) Clustering Arabic Tweets for Sentiment Analysis. 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications, Hammamet, Tunisia., October 30th - November 3rd, 2017.

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Abstract or Summary

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
Keywords that describe the item: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
ID Code:5925
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Deposited On:10 Apr 2018 01:48
Last Modified:10 Apr 2018 01:48

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