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- All Subjects: Data Analysis
COVID-19 has proved that our society can be adaptable in the most unexpected situations. Chaos and fear struck the nation causing people to react in a variety of ways in an attempt to protect their own self interests. The retail space has had to adjust in large scales, making the shopping experience safer both for the customer and the employees. I was able to experience this first hand at Target, working there many years previous to and during the pandemic, getting to see the shift in consumer patterns. I noticed customers would purchase more products in one department, then the next month it would shift to another department. This paper will analyze those shifts in sales trends both departmentaly and within shopping methods at Target to help identify the largest changes and the possible reasons behind these.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.