Review Analysis Using Web Scraping in Python

Analyzing customers’ problems to increase maximum sales on the online market. The description hashtags can be provided here. This work will eradicate the problem of sellers who want to keep their products at the top of any online web applications. This analysis aims to explore the numerous features and build a classification model that can tag the rating based on the description and consequently rate them as positive or negative. This analysis will focus on the score, summary, description and score-based sentiment features. The reviews were gathered from the cloud framework (AWS) and stored the reviews, which were newly assigned through the Webpage that has been developed. The goal was to build a model for text classification by pre-processed data using various techniques and libraries. Then create a word cloud plot based on the high and low scores summary. The pre-processed data is converted to numbers (vectorized) to feed the data into the model. Then train the model and optimize the parameters, which will increase the overall accuracy. After building the classification model, results will be predicted for the test data. The project was developed in Python but used one of the cloud instances like Amazon Web Services, SAS, Google Cloud and Microsoft Azure, to find the best Text Mining tool that fits the project. At last, Python was chosen as the best option and AWS, for instance.