https://github.com/shwetapardhi/project--sentiment_analysis

# Project--Sentiment_Analysis Developed Python script to extract comments data from Amazon and Official site. Performed NLP based Tokenization, Lemmatization, vectorization and processed data in Machine understandable language Have used VADERS, ROBERTA and BERT models to find the sentiment of the reviews and used the ratings on the source to chec
https://github.com/shwetapardhi/project--sentiment_analysis

bert python roberta-model textblob vaders

Last synced: 29 days ago
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# Project--Sentiment_Analysis Developed Python script to extract comments data from Amazon and Official site. Performed NLP based Tokenization, Lemmatization, vectorization and processed data in Machine understandable language Have used VADERS, ROBERTA and BERT models to find the sentiment of the reviews and used the ratings on the source to chec

• Host: GitHub
• URL: https://github.com/shwetapardhi/project--sentiment_analysis
• Owner: shwetapardhi
• Created: 2024-06-11T16:00:26.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-07T09:54:26.000Z (almost 2 years ago)
• Last Synced: 2025-02-27T12:10:15.166Z (over 1 year ago)
• Topics: bert, python, roberta-model, textblob, vaders
• Language: Jupyter Notebook
• Homepage:
• Size: 886 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Project--Sentiment_Analysis

Developed Python script to extract comments data from Amazon and Official site.

Performed NLP based Tokenization, Lemmatization, vectorization and processed data in Machine understandable

language

Have used VADERS, ROBERTA and BERT models to find the sentiment of the reviews and used the ratings on

the source to check the accuracy. also used the textBlob library for processing textual data.

 The proportion clearly shows that the Roberta Pretrained Model performs better than VADER.