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We present a machine learning approach to sentiment classification on twitter messages (tweets). We classify each tweet into two categories: polar and non-polar. Tweets with positive or negative sentiment are considered polar. They are considered non-polar otherwise. Sentiment analysis of tweets can potentially benefit different parties, such as consumers and marketing researchers, for obtaining opinions...
This paper introduces a two-layer stochastic system to diacritize raw Arabic text automatically. The first layer determines the most likely diacritics by choosing the sequence of full-form Arabic word diacritizations with maximum marginal probability via A* lattice search algorithm and m-gram probability estimation. When full-form words are out-of-vocabulary (OOV), the system utilizes a second layer,...
With rapid increase of number of accessible images and videos, ability to recognize visual information is getting more and more important for content-based information retrieval. Recently, probabilistic topic models, which were originally developed for text analysis, have been used for image categorization successfully. Usually, ldquotopicsrdquo which represent contents of an image is detected based...
The goal of this research is to provide an alternative for business processes evaluation and tracking, based on the analysis of non-structured information generated by such processes within the organization areas. In this article we introduce a method to determine the occurrence probability of a business process within the enterprisepsilas text documents. The proposed method introduces the use of...
Due to the vast growth of data collections, the statistical document modeling has become increasingly important in language processing areas. Probabilistic latent semantic analysis (PLSA) is a popular approach whereby the semantics and statistics can be effectively captured for modeling. However, PLSA is highly sensitive to task domain, which is continuously changing in real-world documents. In this...
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