Natural Language Processing-Enhanced Survey Response Analysis Framework for Social Studies

The research has provided a new model of examining the survey results in social research by Sentiment Analysis that is fuelled by Natural Language Processing (NLP). The main objective is to promote the interpretations of the opinions and sentiments of the people that are stated in open survey questions which are sometimes complicated and unstructured. Through sentiment analysis, we are able to derive meaningful information about qualitative survey data and categorize the responses as positive, negative or neutral. The NLP model applied in the given research is the VADER sentiment analysis model, which is effective in working with the social media-like text and survey feedback. This framework enables a researcher to examine vast amounts of survey data in a short amount of time, revealing some latent patterns and trends in responses. The suggested methodology will provide a scalable approach to the analysis of surveys in social research, which will enhance the precision and detail of the results in comparison with the traditional manual coding systems. The findings prove the usefulness of sentiment analysis in the interpretation of complex collective sentiments.