#fuzzy classification

A significant increase has been noticed in the number of people that are utilizing the internet paradigm for various purposes such as accessing various portals such as Social media and E commerce websites. Due to their immense popularity, these platforms have seen remarkable growth and an increasing user base that is constantly interacting on the platform through the use of comments. These comments are mostly the users assisting each other on the platform in making the right decision. These comments can range from helpful to sarcastic, which can be highly difficult for a Natural Language Processing platform to determine. Supervised machine learning approach requires labels such as star ratings in reviews to understand the reviews and classify. These labels need to be reliable, whereas as they are entered by users, they could be misleading. Therefore, in this paper, an unsupervised approach towards the automatic classification of comments has been outlined in much detail. The proposed methodology utilizes an innovative combination of Term Frequency – Inverse Document Frequency TF IDF in addition to the NLP paradigm along with the addition of the Entropy Estimation through Shannon Information gain. This procedure can effectively disintegrate the sentence into its basic form which can then ultimately be classified using the Fuzzy Classification technique. 

Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge in the last half-century. Its theoretical and technological promise is vast, and we are only beginning to experience its potential. Clustering is the first and most basic application of fuzzy set theory, but forms the basis of many, more sophisticated, Fuzzy Sets intelligent computational models, particularly in pattern recognition, data mining, adaptive and hierarchical clustering, and classifier design.Fuzzy Sets and their Application to Clustering and Training offers a comprehensive introduction to fuzzy set theory, focusing on the concepts and results needed for training and clustering applications. It provides a unified mathematical framework for fuzzy classification and clustering, a methodology for developing training and classification methods, and a general method for obtaining a variety of fuzzy clustering algorithms.The authors - top experts from around the world - combine their talents to lay a solid foundation for applications of this powerful tool, from the basic concepts and mathematics through the study of various algorithms, to validity functionals and hierarchical clustering. The result is Fuzzy Sets and their Application to Clustering and Training - an outstanding initiation into the world of fuzzy learning classifiers and fuzzy clustering.