Deep Learning and Sentiment Analysis Improve E-commerce Sales Prediction

The dataset labelled “Amazon and Yelp Reviews“ is a useful resource for sentiment analysis and opinion mining. It has roughly 1.5 million reviews from Amazon and Yelp for a wide range of products and services, as well as the relevant information for each review, which includes product ratings, review dates, and review titles. The dataset's size and diversity make it a good choice for training large-scale deep learning models to handle sentiment analysis tasks. The recommended approach for undertaking sentiment analysis using the “Amazon and Yelp Reviews“ dataset includes three steps: collecting data on a daily basis, preparing user comments, and applying a BiLSTM model for sentiment analysis. Assessing consumer sentiment entails evaluating comments, goods, and businesses to develop predictions about how people feel about various products and services. Some of the measures used in the model evaluation process include accuracy, precision, recall, and F1-score. The proposed method achieves an average accuracy of 87.3%, which is comparable to existing deep learning methods such as character-level convolutional neural networks (CNNs)- LSTM and deep transfer learning. The model's errors may be evaluated to discover where it can be improved, and additional assessment metrics such as AUC- ROC, AUC-PR, G-mean, and MCC can be used to determine how well the model is doing. The dataset and methods presented have the potential to be useful for a wide range of applications, including customer feedback analysis, product and service development, and online reputation management.