Extraction of Emojis and Texts to Intensify Opinion Mining using Machine Learning and Deep Learning Models

This research mainly focuses on product reviews by improving the customer experience. Furthermore, it allows customers to use a better-quality product according to their budget. It allows Businesses to make data-driven decisions about product development, marketing tactics, inventory management, and customer service enhancements by collecting and evaluating sentiment data. This research examines the possibility of emoji-text fusion algorithms to increase sentiment prediction accuracy considering the widespread use of emojis to represent emotion in textual material. A varied dataset of product reviews from Flipkart and Amazon, including a wide range of consumer goods and services, has been put together for this research. This research focuses on sophisticated natural language processing (NLP) methods for text analysis and emoji sentiment classification to accomplish the required goal. To properly capture both textual and emoji-based sentiment cues, This study centers on various feature extraction techniques. Additionally, this study recommends creative fusion along with machine learning and deep learning models such as logistic regression, random forest, BERT, and LSTM to find the polarity of the both texts and emojis, Hence the result is obtained with BERT model with high accuracy including texts with emojis and the outcomes are obtained using proper visualization techniques.