Poetry Sentiment Analyzer

A web application that analyzes the emotional content of poetry using Natural Language Processing (NLP) techniques.

Features

• Sentiment Analysis: Analyze poems to detect six emotional categories (Anger, Disgust, Fear, Joy, Neutral, Sadness)
• Visualization: View the breakdown of different emotions detected in the text
• NLP Processing: Uses tokenization, stemming, and TF-IDF for text analysis
• User-Friendly Interface: Simple UI for entering poems and viewing results

Technologies Used

• Next.js 14 with App Router
• TypeScript
• TailwindCSS for styling
• Natural.js for NLP functionality
• TF-IDF for feature extraction

Getting Started

Prerequisites

• Node.js 18 or later
• npm or yarn

Installation

1. Clone the repository
2. Install the dependencies:

npm install
# or
yarn install

3. Run the development server:

npm run dev
# or
yarn dev

4. Open http://localhost:3000 in your browser

Project Structure

• app/: Next.js app directory
  • components/: UI components
  • data/: Sample training data for sentiment analysis
  • lib/: Sentiment analysis algorithms
  • api/: API routes for poem analysis

How It Works

The sentiment analyzer uses a combination of techniques to determine the emotional content of poetry:

1. Preprocessing: The text is tokenized, stopwords are removed, and tokens are stemmed.
2. Feature Extraction: TF-IDF is used to identify important words in the text.
3. Classification: The preprocessed text is compared against feature words for each emotion.
4. Scoring: Scores are calculated for each emotional category, and the dominant emotion is identified.

Extending the Application

To improve the sentiment analysis model:

1. Increase the training dataset size with more examples of each emotion
2. Implement more sophisticated ML models (e.g., neural networks)
3. Add additional features like n-grams or word embeddings
4. Integrate with external NLP APIs for more robust analysis

License

MIT