Sentiment Analysis Using an Ensemble Approach on Flipkart Societal Media Data

The analysis of sentiments, especially with social media data, has become increasingly important over the years. It offers insights into opinions, consumer behaviour and societal trends. However, existing methods for sentiment analysis primarily focus on identifying negative sentiments rather than categorizing the complex range of distinct and diverse emotions that humans experience. This study introduces an approach to categorizing sentiments in social media data to overcome the limitations of existing methods and enhance the accuracy of sentiment classification. By filling this gap in research, our proposed method provides an understanding of the sentiments expressed on the Flipkart E-commerce website. Our research effectively addresses existing limitations through an ensemble learning model and achieves an impressive 99% accuracy on our test dataset.