DATA_SCIENCE_PROJECT
Python
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Twitter Sentiment Analysis
Twitter Sentiment Analysis
Natural Language Processing (NLP) project that analyzes the sentiment (Positive, Negative, Neutral) of tweets.
Key Features
- Tweet Scraping: Fetches tweets using API or scraper.
- Text Preprocessing: Tokenization, stop-word removal, and stemming.
- Sentiment Classification: Uses Naive Bayes or Transformer models (BERT).
- Visualization: Word clouds and sentiment distribution charts.
Technology Stack
- NLP: NLTK, Spacy, Transformers
- Language: Python
- Model: BERT / RoBERTa
Installation
- Install Python dependencies.
- Run the sentiment analyzer on your dataset or live input.
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Project Description
Twitter Sentiment Analysis
Natural Language Processing (NLP) project that analyzes the sentiment (Positive, Negative, Neutral) of tweets.
Key Features
- Tweet Scraping: Fetches tweets using API or scraper.
- Text Preprocessing: Tokenization, stop-word removal, and stemming.
- Sentiment Classification: Uses Naive Bayes or Transformer models (BERT).
- Visualization: Word clouds and sentiment distribution charts.
Technology Stack
- NLP: NLTK, Spacy, Transformers
- Language: Python
- Model: BERT / RoBERTa
Installation
- Install Python dependencies.
- Run the sentiment analyzer on your dataset or live input.
Technologies Used
Python
NLTK
Bert
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