Automatic Valuation of Essay using Machine Learning

Abstract

Giving ratings to essays by humans is laborious and prone to mistakes. Although so much exploration is done to evaluate an essay, not so much is done to analyze an essay using syntax, semantic and sentiment analysis. Existing systems use several supervised and unsupervised methodologies to assess text coherence. Typically, unsupervised approaches assess lexical cohesiveness or the frequency with which phrases in an essay appear. It checks grammar, coherence, and semantics in a phrase. In the Essay, we have covered graph- based linkages and statement’s polarity opinion. To construct Graph-based spatial links and unique characteristics. There is a semantic resemblance established between every statement of the essay. The graph-based representation aids in the discovery of patterns and correlations within a set of data. The design and Information of documents can be constitute using a graph representation of text. Subsequently, the structure depends on little features which give a system more predictive power. Because of very few features, we can resist noisy data, and remove redundancy. We had taken some datasets from Kaggle and populated some data by taking different Essays from the Internet. We predict the score using XGBoosthis electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.