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Movie Sentiment Analysis and Clip Extraction

This project performs sentiment analysis on movie subtitles and provides a way to extract clips based on the analyzed sentiments. It uses a pre-trained sentiment analysis model to classify emotions in subtitles and visualizes the sentiment trends over time.

Features

  • Sentiment Analysis: Analyzes subtitles to classify emotions such as joy, sadness, anger, etc.
  • Visualization: Plots sentiment scores over time to show emotional trends in the movie.
  • Clip Extraction: Enables identification of specific scenes or clips based on sentiment.

Technologies Used

  • Python: Core programming language.
  • Transformers: For sentiment analysis using the michellejieli/emotion_text_classifier model.
  • Pandas: For data manipulation and analysis.
  • Matplotlib & Seaborn: For data visualization.
  • MoviePy: (Planned) For extracting video clips based on sentiment.

Installation

  1. Clone the repository:

    git clone https://github.com/ZeMendes17/kurz.git

  2. Run Docker Compose

    docker compose up --build

  3. Ensure you have the subtitle dataset (both .csv files) in the dataset/ folder inside kurz/src/kurz and kurz-recommendation-api/src/. The dataset should include a CSV file with subtitles and their corresponding movie IDs. The dataset is: https://www.kaggle.com/datasets/adiamaan/movie-subtitle-dataset.

Example Output

  • A line plot showing sentiment scores across the movie's subtitles.
  • Sentiment trends that can help identify emotional highs and lows in the movie.

Acknowledgments

  • Hugging Face for the sentiment analysis model.
  • Open-source libraries like Pandas, Matplotlib, and Seaborn for data processing and visualization.