Text clustering is a critical step in text data analysis and has been extensively studied by the mining community. Most existing algorithms are based on bag-of-words model, which faces high-dimensional sparsity problems ignores structural sequence information. Deep learning-based models such as convolutional neural networks recurrent regard texts sequences but lack supervised signals explainabl...

User awareness has become a popular feature in many social web applications. In classic text-based web-systems, user awareness features show how many users are online in a web application or how many users are accessing the same web page. When time-based media like web lectures are concerned this approach comes to its limits since time-based media are inherently different from classic text-base...

Dual-interaction spaces—that combine text chat with a shared graphical work area—have been developed in recent years as CSCL applications to support the synchronous construction and discussion of shared artifacts by distributed small groups of students. However, the simple juxtaposition of the two spaces raises numerous issues for users: How can objects in the shared workspace be referenced fro...

The performance of text classification methods has improved greatly over the last decade for instances less than 512 tokens. This limit been adopted by most state-of-the-research transformer models due to high computational cost analyzing longer instances. To mitigate this problem and improve texts, researchers have sought resolve underlying causes proposed optimizations attention mechanism, wh...

This paper proposes a new approach for text categorization, based on a feature projection technique. In our approach, training data are represented as the projections of training documents on each feature. The voting for a classification is processed on the basis of individual feature projections. The final classification of test documents is determined by a majority voting from the individual ...

Text categorization is an important application of machine learning to the field of document information retrieval. Most machine learning methods treat text documents as a feature vectors. We report text categorization accuracy for different types of features and different types of feature weights. The comparison of these classifiers shows that stemmed or un-stemmed single words as features giv...