Arabic Document Classification Using Multiword Features

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We introduce a list of Arabic multiword expressions (MWE) collected from various dictionaries. The MWEs are grouped based on their syntactic type. Every constituent word in the expressions is manually annotated with its full context-sensitive morphological analysis. Some of the expressions contain semantic variables as place holders for words that play the same semantic role. In addition, we ha...

Arabic Handwritten Word Category Classification Using Bag of Features

Human writing is highly variable and inconsistent, and this makes the offline recognition of handwritten words extremely challenging. This paper describes a novel approach that can be employed for the offline recognition of handwritten Arabic words. Through conceptualizing each word as single, inseparable objects, the proposed approach aims to recognize words in accordance with their complete s...

Features for Unsupervised Document Classification

Machine Learning approach to Document Classification using Concept based Features

Text mining refers to the process of deriving high-quality information from text. Text processing involves in search and replace in electronic format of text. A number of approaches have been developed to represent and classify text documents. Most of the approach tries to attain good classification performance while taking a document only by words. We propose a concept based methodology instea...

Sentiment Document Classification Using Global and Domain Features

The goal of sentiment classification is to detect writer’s sentiment from a document. This paper investigates which features and what combination of them is more effective in sentiment classification. Experiments show that the effective combination method of global and domain features can significantly reduce classification errors relative to features which have been used in general text classi...