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.
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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