Stakeholders Sentiment Analysis Using Twitter Data on NEP 2020: A Case Study Approach

The National Education Policy 2020 (NEP2020) is a comprehensive framework aimed at transforming the education system in India. This paper analyzes NEP2020 and its details envisioned in promoting educational excellence and addressing the needs of a rapidly changing world. The educated elite have access to the information on salient features and hence are in a position to give their free and fair opinion. Thus, this work is based on twitter wherein it utilizes a dataset of 18000 tweets, focusing on discussions and opinions related to the NEP 2020. The tweets are preprocessed and then labeled with sentiment categories such as positive, negative, or neutral. A decision tree classifier is trained on the preprocessed and labeled dataset to predict the sentiment of new tweets. The performance of the classifier is assessed using accuracy, recall, F1 score, and precision metrics, providing insights into the effectiveness of the sentiment analysis approach. The decision tree classifier achieves promising performance in accurately predicting sentiment categories. It is observed that over 55% of tweets are positive towards the policy, 17% are negative and 28% are neutral towards the policy with the study being 92% accurate. The findings of this work can be used by policymakers and education stakeholders in understanding public sentiment and making informed decisions regarding the NEP 2020.