Automatic Classification of Research Proposal

Text Mining (TM) Techniques are practically used in web applications, academia, and internet industry and other field also. In the research field, it support to identify and categorise research works and related notes of many domains can get from the individual place itself. If a group of proposals belongs to a particular research area and it may contain a large number of proposals, and these proposals are grouped and provide them to the reviewer. Ontology method is a possible technique in this area. Text-mining methods are used to solve the problem of classifying text documents automatically. In this work, research proposals are classified based on the discipline areas, and proposals in each discipline are grouped using the text-mining technique. In phase 1, Preprocessing techniques such as word extraction, stop words, stemming and term frequency-inverse document frequency (TF/IDF) is used to tokenise, stem the word and count the frequency of the word present in the research proposal. In phase 2, the approach is proposed to organise the list of MWTs extracted automatically from a domain specific corpus of text documents into a hierarchy based on their semantic similarity.