Classifying sentiment in arabic social networks: Naïve search versus Naïve bayes

  title={Classifying sentiment in arabic social networks: Na{\"i}ve search versus Na{\"i}ve bayes},
  author={Mazen Itani and Lama Hamandi and Rached N. Zantout and Islam Elkabani},
  journal={2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)},
Social networks contain large amounts of posts of different data types (text, images, sounds and videos). Textual posts express authors' opinions (with or against) or feeling (love, hate, optimism, pessimism, or anger). Such opinions are important for commercial and governmental organization since they help checking public opinion about a product, policy or an object in general. In this paper we present the application of two different approaches to classify Arabic Facebook posts. The first one… CONTINUE READING
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