A Data Science Based Approach for Sentiment Classification of Social Media Data

A Data Science Based Approach for Sentiment Classification of Social Media Data

Jyoti Bamblani1, Prof. Pooja Hardiya2

Abstract— Sentiment classification is a crucial task in natural language processing (NLP) and data science that involves determining the sentiment or emotion conveyed in a piece of text. It has widespread applications in social media analysis, customer feedback evaluation, and financial forecasting. In this approach, the customer review dataset from Amazon reviews has been analyzed. Pre-processing of raw data has been done prior to using it to train a neural network. The regularization based Bayes Optimized Deep Neural Network has been used for sentiment classification from social media dataset. To compare the performance of the proposed system against existing research in the domain, the predication error metric has been computed. From the performance of the proposed system, it can be observed that the proposed system clearly outperforms the existing approaches in terms of prediction error and accuracy.

Keywords—Natural Language Processing (NLP), Sentiment Analysis, Deep Neural Networks, Regulrization, Prediction Error.