In this paper, we introduce an algorithm for grouping Arabic
documents for building an ontology and its words. We execute
the algorithm on five ontologies using Java. We manage the
documents by getting 338667 words with its weights
corresponding
to each ontology. The algorithm had proved its
efficiency in optimizing classifiers (SVM, NB) performance, which
we tested in this study, comparing with former classifiers results
for Arabic language.
Text classification is one of the important areas in natural language processing. The classification problem has been widely studied in data extraction, automated learning, database, and information retrieval with applications in many diverse fields,
such as target marketing, medical diagnosis, newsgroup filtering, document organization, topic identification, . For example, in areas such as Computer Vision, there is a strong consensus on a general way of designing models, neural networks, and other approved methodologies. Otherwise, the classification of the text still lacks this general approach in many areas. In this paper, we aim to provide a comprehensive survey of a variety of methodologies and algorithms used to classify texts and their improvements. We will focus on the main general approaches to text classification algorithms and their usage cases.
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, r
anging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.
WARNING: This article contains contents that may offend the readers. Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand t
he genuine semantics. In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose. Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as Peers' and Others'. We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.
