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NLTK Sentiment Analyzer

This project is a simple but effective sentiment analysis tool built using Python and the Natural Language Toolkit (NLTK). It's trained on a dataset of movie reviews to classify a piece of text as either "Positive" or "Negative".

Project Overview

The script performs the following steps:

1. Loads Data: It uses the movie_reviews corpus included with NLTK.
2. Text Preprocessing: Cleans text by tokenizing, converting to lowercase, and removing stopwords and punctuation.
3. Feature Extraction: Uses a "Bag of Words" approach based on the 3,000 most frequent words.
4. Model Training: A Naive Bayes classifier is trained on 95% of the dataset.
5. Evaluation: The model is evaluated on the remaining data to check its accuracy.
6. Prediction: A function is provided to predict the sentiment of any new sentence.

Technologies Used

• Python 3
• NLTK (Natural Language Toolkit)
• Pandas
• Scikit-learn

How to Run

1. Navigate to the project directory:

cd NLTK-Sentiment-Analyzer

2. Install dependencies:

pip install -r requirements.txt

3. Run the script:

python sentiment_analyzer.py

The first time you run the script, it will automatically download the necessary NLTK data packages.

Sample Output

Accuracy: 85.0%

---
Sentence: "This movie was absolutely fantastic! The acting was superb."
Sentiment: Positive
---
Sentence: "I did not like the film. It was boring and slow."
Sentiment: Negative