Ecommerce Sentiment Analysis

• Tech Stack: Python, Tensorflow, pandas, Numpy
• Github: Project Link

Developed Ecommerce Sentiment Analysis Platform Using AI
Conceived and built a specialized sentiment analysis platform designed for the ecommerce sector. The system employs advanced machine learning algorithms to assess customer sentiment based on reviews and comments, providing invaluable insights into customer satisfaction and product quality.

Data Acquisition through Web Scraping
Faced with a data shortage, I designed a web scraper tool capable of automatically fetching customer comments from various ecommerce sites. This data collection method significantly expanded the available dataset, enhancing the model's training and eventual accuracy.

Data Cleaning and Preprocessing
Executed a rigorous data cleaning and preprocessing pipeline to prepare the acquired data for machine learning algorithms. This involved removing irrelevant or redundant information, as well as formatting the data into a usable structure, thereby improving the quality of the insights generated.

AI Model for Sentiment Analysis
Employed a sophisticated artificial intelligence model to analyze the cleaned and formatted data, effectively identifying customer sentiment with high accuracy. The results of this analysis serve as a crucial tool for ecommerce businesses looking to improve their products and services based on customer feedback.