Subjective Answer Evaluation Using Machine Learning And Natural Language Processing

Abstract

The manual evaluation of subjective papers is a difficult and time-consuming task. In education and other professions, evaluating subjective responses is a crucial activity, but it is frequently subjective and time-consuming. In this study, we suggest a machine-learning and natural language processing-based automated method for assessing subjective responses. The system will employ NLP approaches to extract variables like word frequency, phrase length, and sentiment analysis after being trained on a dataset of graded subjective replies. The grades of fresh subjective answers will subsequently be predicted using machine learning methods based on these extracted features.We will compare our system's performance to other methods already in use for evaluating subjective answers using measures like accuracy, precision, and recall.Additionally, we will conduct cross-validation to make sure that our model applies well to fresh data.The project has the ability to provide objective and consistent evaluations while also drastically reducing the time and effort needed for subjective answer evaluation. It can be used in a variety of ways in education and other industries, like e-learning tools and online education platforms. By using these technologies to address a real-world issue, it can also advance the fields of machine learning and NLP.Using cutting-edge machine learning and NLP approaches, this project seeks to automate and enhance the subjective answer evaluation process in order to increase its effectiveness, accuracy, and dependability.