Welcome to the world of e-commerce, where customer feedback is a goldmine of insights! In this project, you'll dive into the Women's Clothing E-Commerce Reviews dataset, focusing on the 'Review Text' column filled with direct customer opinions.
Your mission is to use text embeddings and Python to analyze these reviews, uncover underlying themes, and understand customer sentiments. This analysis will help improve customer service and product offerings.
The Data
You will be working with a dataset specifically focusing on customer reviews. Below is the data dictionary for the relevant field:
womens_clothing_e-commerce_reviews.csv
| Column | Description |
|---|---|
'Review Text' | Textual feedback provided by customers about their shopping experience and product quality. |
Armed with access to powerful embedding API services, you will process the reviews, extract meaningful insights, and present your findings.
Let's get started!
Install useful libraries
# Run this cell to install ChromaDB if desired
try:
assert version('chromadb') == '0.4.17'
except:
!pip install chromadb==0.4.17
try:
assert version('pysqlite3') == '0.5.2'
except:
!pip install pysqlite3-binary==0.5.2
__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
import chromadbLoad the dataset
Load data and perform basic data checks to ensure you are using relevant data for the analysis
# Load the dataset
import pandas as pd
reviews = pd.read_csv("womens_clothing_e-commerce_reviews.csv")
# Display the first few entries
reviews.head()# Start coding here
# Use as many cells as you need.