Please use this identifier to cite or link to this item:
|Title:||Clustering Arabic tweets for sentiment analysis|
Language and emotions - Arab countries
Similarity (Language learning)
|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 normalsized documents where, in many information retrieval applications, light stemming performs better than rootbased stemming and the Cosine function is commonly used|
|Appears in Collections:||Fulltext Publications|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.