An Introduction to a New Text Classification and Visualization for Natural Language Processing Using Topological Data Analysis
This is an incremental application of existing TDA methods to a new domain-specific dataset in NLP.
The paper tackles text classification of Persian poems by applying Topological Data Analysis (TDA) methods, specifically Persistent Homology and Mapper, to classify works by poets Ferdowsi and Hafez, but does not provide concrete numerical results.
Topological Data Analysis (TDA) is a novel new and fast growing field of data science providing a set of new topological and geometric tools to derive relevant features out of complex high-dimensional data. In this paper we apply two of best methods in topological data analysis, "Persistent Homology" and "Mapper", in order to classify persian poems which has been composed by two of the best Iranian poets namely "Ferdowsi" and "Hafez". This article has two main parts, in the first part we explain the mathematics behind these two methods which is easy to understand for general audience and in the second part we describe our models and the results of applying TDA tools to NLP.