Evaluation of Subjective Answers Using Machine Learning

Abstract

Negative methods are currently used to evaluate subjective writing. The evaluation of the subjective responses is an essential responsibility. When a human analyses anything, the evaluation's quality can change depending on the person's emotions. All outcomes in machine learning are solely dependent upon the user's input data. To address this issue, our suggested method combines machine learning (ML) and natural language processing (NLP). To analyse the subjective response, our algorithm performs tasks including tokenizing words and phrases, classifying parts of speech, chunking and chinking, lemmatizing words, and word netting. Our suggested approach also offers the context's semantic meaning. There are two modules in our system. Extracting data from scanned photos is the initial step. Then arranging it properly, and the second is using ML and NLP to analyse the text obtained in the previous phase and assigning grades to it.