Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for Analyzing Product Sentiments in Online Business in Nigeria

In today's e-commerce in Nigeria, customers access online stores to browse through and place orders for products or services via the internet on their devices while some are skeptical due to the experiences from what I ordered versus what I got syndrome. Though this method of business has flourished to an extent, it greatly faces a crucial challenge in unravelling consumer's sentiments particularly in the realm of product reviews. This deficiency inhibits most e-commerce platforms in Nigeria from gaining effective sensitivity into users' preferences, thus, limiting their ability to boost their product recommendations and, understand and improve customers' experiences. This research aims to bridge this gap by developing a sentiment analyzer of product in the e-commerce domain using Natural language processing and machine learning approach. The model will analyze the customers' reviews based on positive or negative. The experimental data was collected from kaggle.com. Stemming and lemmatization were approaches used for cleaning the collected data. Features were extracted and transformed using CountVectorizer. Gaussian Naïve Bayes classifier was used as the machine learning technique. The model's performance was evaluated and it returned 90% of accuracy, hence, an efficient and reliable model for product review sentiment analysis is developed.