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https://github.com/raksh710/covid-19_tweets_sentiment_analysis
Predicted the sentiment associated with tweets made on the topic of Covid-19 pandemic. Tweets were classified into "Positive", "Extremely Positive", "Neutral","Negative" and "Extremely Negative". TF-IDF Vectorization was used to vectorize the tokens present in the tweets and then to classify "CatBoost" algorithm was used. Ultimately achieving an accuracy of around 57%.
https://github.com/raksh710/covid-19_tweets_sentiment_analysis
Last synced: about 1 month ago
JSON representation
Predicted the sentiment associated with tweets made on the topic of Covid-19 pandemic. Tweets were classified into "Positive", "Extremely Positive", "Neutral","Negative" and "Extremely Negative". TF-IDF Vectorization was used to vectorize the tokens present in the tweets and then to classify "CatBoost" algorithm was used. Ultimately achieving an accuracy of around 57%.
- Host: GitHub
- URL: https://github.com/raksh710/covid-19_tweets_sentiment_analysis
- Owner: Raksh710
- Created: 2022-01-05T23:00:50.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-05T23:23:24.000Z (almost 3 years ago)
- Last Synced: 2023-03-30T20:15:15.305Z (over 1 year ago)
- Language: HTML
- Size: 624 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0