Text Classification In Poetry

Introduction

Text classification is the act of dividing a set of input documents into two or more classes where each document can be said to belong to one or multiple classes [5]. Text Classification is a text mining technique which is used to classify the text documents into predefined classes.
A poem is a piece of writing in which the expression of feelings and ideas is given intensity by particular attention to diction, rhythm, and imagery [6]. It is generally meant to deliver expressions such as love, happiness, success, fear, sadness etc. An Automatic poetry classification takes a poem as an input and identifies its category as its output. An Automatic Poetry Classification is a Text classification problem
Punjabi language is one of

Methodology Used

Text classification method is a kind of supervised learning methods Text classification method is to construct a classification model, and thus determine which type the samples belong to which class. There are many ways to construct the current classification and the commonly used classification methods based on statistical are Bayes classifier [5] [7] [8], KNN, support vector machine, decision tree, regression model. Naive Bayes has very high learning efficiency and it can estimate all the probability just need a scan of the training data. It can also be used as an incremental
Algorithm. When new data emerge, model can be updated and then the probability value can be updated easily. So Naïve Bayes text classification has been widely used. Bayes theory is a process of statistical inference which means taking into account general information and prior information to obtain posterior information. Its main feature is the use of probability to denote all forms of uncertainty and through probabilistic rules to achieve learning and reasoning. Bayes theory is by calculating the frequency of occurrence of something in the past to estimate the future probability of its occurrence.

It is "prior" in the sense that it does not take into account any information about X. P (Y|X) is the conditional probability of Y, given X. It is also called the posterior probability because it is derived from or depends upon the specified value of X. P (X|Y) is the conditional probability of X given Y. It is also called the likelihood. P(X) is the prior or marginal probability of X, and acts as a normalizing constant. Great posterior hypothesis, in many cases, given a data D, need to find the conditional probability P (c|D) in the candidate set C. Suppose the maximum c ∈ C. Any assumptions like that have the greatest likelihood will be called as