Text classification aims to assign labels textual units such as documents, sentences and paragraphs. Some applications of text include sentiment news categorization. In this paper, we present a soft computing technique-based algorithm (TSC) classify polarities tweets well categories from text. The TSC is supervised learning method based on tolerance near sets. Near sets theory more recent metho...

Short text is ambiguous and often relies predominantly on the domain context at hand in order to attain semantic relevance. Existing classification models perform poorly short due data sparsity inadequate context. Auxiliary context, which can provide sufficient background regarding domain, typically available several application scenarios. While some of existing works aim leverage real-world kn...

A semantic-extension-based algorithm for short texts is proposed, by involving the Word2vec and LDA model, to improve performance of classification, which frequently deteriorated semantic dependencies scarcity features. For every keyword within a text, weighted synonyms related words can be generated Word2Vec respectively, subsequently inserted extend text reasonable length. We not only have es...

Measuring the semantic similarity between words requires a method that can simulate human thought. The use of computers to quantify and compare similarities has become an important research area in various fields, including artificial intelligence, knowledge management, information re-trieval, natural language processing. Computational seman-tics require efficient measures for computing concept...