Inspecting Hybrid Data Mining Approaches in Decision Support Systems for Humanities Texts Criticism

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated automatically-supported decisions. However, these may face challenges difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting summarizing relevant from large text documents colossal content. When obtaining online, instance social networking media, sites undergo remarkable increase textual main objective present study is conduct survey show latest developments about implementation text-mining techniques humanities when This process relies on technological advancement considers (1) automated-decision support-techniques commonly used humanities, (2) performance evolution use stylometric approach text-mining, (3) comparisons results chunking using different attributes Burrows' Delta method. also provides an overview efficiency applying some selected data-mining (DM) methods various support critics' artistry ‒ one field humanities. automatic choice criticism this was supported hybrid procedures.