Clustering Arabic Tweets for Sentiment Analysis

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

Clustering Arabic Tweets for Sentiment Analysis

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

Learning probabilistic relational models with (partially structured) graph databases

International audienceProbabilistic Relational Models (PRMs) such as Directed Acyclic Probabilistic Entity Relationship (DAPER) models are probabilistic models dealing with knowledge representation and relational data. Existing literature dealing with PRM and DAPER relies on well structured relational databases. In contrast, a large portion of real-world data is stored in Nosql databases specially graph databases that do not depend on a rigid schema. This paper builds on the recent work on DAPER models, and describes how to learn them from partially structured graph databases. Our contribution is twofold. First, we present how to extract the underlying ER model from a partially structured graph database. Then, we describe a method to compute sufficient statistics based on graph traversal techniques. Our objective is also twofold: we want to learn DAPERs with less structured data, and we want to accelerate the learning process by querying graph databases. Our experiments show that both objectives are completed, transforming the structure learning process into a more feasible task even when data are less structured than an usual relational database