GitHub - martamanevska/Python-Sentiment-Analysis-Web-Scraping-: By utilizing web scraping techniques, we were able to observe the similarities and differences between both newspapers, when it comes to main keywords, topics, and even the sentiment expressed. How news outlets on both sides of the conflict portray the Ukrainian War, adding a third one that is supporting the Russian narrative in Europe.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
BbcArticles.csv		BbcArticles.csv
FrequencyAnalysis.ipynb		FrequencyAnalysis.ipynb
HotelOnlineReviews.txt		HotelOnlineReviews.txt
IntroToTextMining - CNN articles.ipynb		IntroToTextMining - CNN articles.ipynb
IntroToTextMining.ipynb		IntroToTextMining.ipynb
KeywordsExtraction - CNN articles.ipynb		KeywordsExtraction - CNN articles.ipynb
KeywordsExtraction.ipynb		KeywordsExtraction.ipynb
NamedEntityRecognition - CNN articles.ipynb		NamedEntityRecognition - CNN articles.ipynb
NamedEntityRecognition.ipynb		NamedEntityRecognition.ipynb
NetworkMeasures.ipynb		NetworkMeasures.ipynb
SMA Report.docx.pdf		SMA Report.docx.pdf
SMA Report_present.docx.pdf		SMA Report_present.docx.pdf
SentimentAnalysis - CNN articles.ipynb		SentimentAnalysis - CNN articles.ipynb
SentimentAnalysis -Sputnik snappy.ipynb		SentimentAnalysis -Sputnik snappy.ipynb
SentimentAnalysis.ipynb		SentimentAnalysis.ipynb
TextAnnotation - CNN articles.ipynb		TextAnnotation - CNN articles.ipynb
TextAnnotation.ipynb		TextAnnotation.ipynb
The difference between Russian and Western media in the coverage of the Ukrainian War.pdf		The difference between Russian and Western media in the coverage of the Ukrainian War.pdf
TopicModeling - CNN articles.ipynb		TopicModeling - CNN articles.ipynb
Web Scraping News + Sentiment_Name-Recognition_Frequency Analysis.docx.pdf		Web Scraping News + Sentiment_Name-Recognition_Frequency Analysis.docx.pdf
bbc_links.pkl		bbc_links.pkl
scraper_bbc - Copy.ipynb		scraper_bbc - Copy.ipynb
scraper_bbc.ipynb		scraper_bbc.ipynb
scraper_cnn.ipynb		scraper_cnn.ipynb
scraper_falso.ipynb		scraper_falso.ipynb
scraper_reuters.ipynb		scraper_reuters.ipynb
scraper_sun.ipynb		scraper_sun.ipynb
scraper_the_economist.ipynb		scraper_the_economist.ipynb

About

By utilizing web scraping techniques, we were able to observe the similarities and differences between both newspapers, when it comes to main keywords, topics, and even the sentiment expressed. How news outlets on both sides of the conflict portray the Ukrainian War, adding a third one that is supporting the Russian narrative in Europe.

edition.cnn.com/ https://www.economist.com/

python html text-mining sentiment-analysis named-entity-recognition topic-modeling text-annotation keywords-extraction