brycenab/quotes_toscrape_sentimentanalysis

example code fro scraping quotes.toscrape.com and using NLTK to analyze the sentiment for each quote.

Sentiment Analysis of Scraped Quotes

Perform sentiment analysis on a collection of scraped quotes using Natural Language Processing (NLP) techniques and the NLTK library.

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Introduction

This project demonstrates how to perform sentiment analysis on a set of quotes that have been scraped from a website. The sentiment analysis is based on the sentiment intensity provided by the NLTK library's Sentiment Intensity Analyzer. The quotes are preprocessed by tokenization, removing punctuation, stopwords, and lemmatization before performing sentiment analysis.

In addition to sentiment analysis, this project includes an example of how to scrape quotes from a target website. The scraping process involves fetching HTML content, parsing it using the BeautifulSoup library, and extracting relevant information such as quotes, authors, and tags.

Dependancies

pip install nltk 

pip install beautifulsoup4

pip install requests

pip install pandas

Output

The "main.py" script will create a CSV file named "quotes.csv", which will contain the following information for each quote:

• Quote
• Author
• Tags

The "SentimentAnalysis.py" script will analyze the quotes and give them a score based on the language used, based on the score it will either be classified as "Positive", "Neutral", or "Negative". Then a new CSV file will be added named "quotes_with_sentiment.csv" adding the "Sentiment" column.

Note

This script is provided for educational purposes only and is meant to demonstrate basic web scraping techniques. Before scraping any website, please review the website's Terms of Service and Robots.txt file to ensure you are not violating any rules or policies.