Sentiment Analysis of Flipkart Reviews

This Python project performs sentiment analysis on Flipkart product reviews using various libraries and tools including pandas, numpy, nltk's SentimentIntensityAnalyzer, wordcloud, seaborn, and matplotlib.pyplot. The goal is to analyze and visualize the sentiment distribution of reviews for a better understanding of customer opinions.

Table of Contents

• Importing libraries
• Data Preparation
• Sentiment Analysis
• Data Visualization
• Contributing

Importing libraries

To start with the project we need to import all the necessary libraries needed .

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator

Data Preparation

Before performing sentiment analysis, you need to collect Flipkart reviews data. You can manually copy and paste reviews from the Flipkart website into a CSV file or use web scraping techniques to automate data collection. Ensure that the CSV file has at least the following columns:

• Review Text: The text of the review.
• Rating: The numerical rating given by the user (e.g., 1-5).

Sentiment Analysis

The sentiment analysis is performed using the SentimentIntensityAnalyzer from the NLTK library. It assigns sentiment scores to each review, indicating whether the review is positive, negative, or neutral. The script calculates the following sentiment scores for each review:

• Compound Score: A normalized compound score ranging from -1 (most negative) to 1 (most positive).
• Positive Score: The positive sentiment score.
• Negative Score: The negative sentiment score.
• Neutral Score: The neutral sentiment score.

The script then classifies each review as either positive, negative, or neutral based on the compound score.

Data Visualization

After performing sentiment analysis, the script generates various visualizations using wordcloud, seaborn, and matplotlib.pyplot. These visualizations include:

• Word Cloud: A word cloud visualization of the most frequently occurring words in positive and negative reviews.
• Sentiment Distribution: A bar chart showing the distribution of sentiment (positive, negative, neutral) in the reviews.
• Rating vs. Sentiment: A box plot showing the relationship between product ratings and sentiment.

Contributing

If you'd like to contribute to this project, please fork the repository and create a pull request. You can also open issues for bug reports or feature requests.