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https://github.com/gracysapra/sentiment-analysis-on-twitter-data
This project applies sentiment analysis on Twitter data, classifying tweets as positive, negative, or neutral using machine learning models and BERT. It includes data cleaning, TF-IDF vectorization, and data augmentation techniques to enhance model performance.
https://github.com/gracysapra/sentiment-analysis-on-twitter-data
adaboost bert classification data-augmentation data-cleaning data-preprocessing data-science datasets deep-learning logistic-regression machine-learning python random-forest-classifier sentiment-analysis svm twitter vectorization
Last synced: 8 days ago
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This project applies sentiment analysis on Twitter data, classifying tweets as positive, negative, or neutral using machine learning models and BERT. It includes data cleaning, TF-IDF vectorization, and data augmentation techniques to enhance model performance.
- Host: GitHub
- URL: https://github.com/gracysapra/sentiment-analysis-on-twitter-data
- Owner: Gracysapra
- Created: 2024-10-16T08:45:26.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-16T08:50:40.000Z (3 months ago)
- Last Synced: 2024-11-11T22:39:35.617Z (2 months ago)
- Topics: adaboost, bert, classification, data-augmentation, data-cleaning, data-preprocessing, data-science, datasets, deep-learning, logistic-regression, machine-learning, python, random-forest-classifier, sentiment-analysis, svm, twitter, vectorization
- Language: Jupyter Notebook
- Homepage:
- Size: 2.17 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0