This project demonstrates how to perform sentiment analysis using deep learning on Amazon product reviews dataset. The dataset used for the project is obtained from Kaggle and consists of nearly 3000 reviews of amazon users regarding various amazon Alexa products like Alexa echo, Alexa dot etc. Exploratory data analysis is performed on the dataset to analyse various columns and the data is visualized using count plots and pie charts. The reviews are then processed using various methods which involve lowercase conversion, URL removal, punctuation removal, tokenization, stop word removal and stemming. The processed data is then separated into positive and negative reviews and are then visualized using Word clouds, as word clouds help to identify the most prominent/frequently used words. The processed data is then passed into a neural network, where the network learns from the data. The accuracy of the model is then measured by running the model on the test data.
To see the complete video explanation of the topic, check out the following link: https://youtu.be/6dHzwavIQ58