Introduction to MultiSentimentArcs

Understanding the emotional trajectory of a story—whether in a novel, a film, or a social media thread—has long been a goal of both literary scholars and data scientists. MultiSentimentArcs is a groundbreaking open-source project that bridges the gap between Digital Humanities and advanced Artificial Intelligence. Developed to visualize multimodal sentiment arcs in long-form narratives, this tool leverages the power of Large Language Models (LLMs) and Large Multimodal Models (LMMs) to analyze complex emotional landscapes.

Unlike traditional sentiment analysis that often relies on simple dictionary lookups, MultiSentimentArcs integrates state-of-the-art models like Llama 3, Mistral, and Phi-3 to interpret nuances across different modalities, such as video and text. Whether you are a researcher analyzing Hollywood films or a developer exploring affective computing, this framework provides a robust method for visualizing how sentiment evolves over time.

Key Features

MultiSentimentArcs stands out for its ability to handle complex, long-form content on accessible hardware. Here are its primary features:

• Multimodal Analysis: Capable of analyzing both textual transcripts and visual video frames to create a holistic view of narrative sentiment.
• Advanced Model Integration: Includes support for cutting-edge open-source AI models, including Llama 3, LLaVA, Mistral 7B, and Phi-3.
• Visualizations: Generates detailed sentiment arcs and Kernel Density Estimation (KDE) distribution plots to visualize emotional peaks and valleys.
• Consumer Hardware Friendly: Designed to run on mid-range consumer gaming laptops, democratizing access to high-level affective AI research.
• Time Series Transformations: Applies sophisticated smoothing and transformation techniques to raw sentiment data to reveal underlying narrative structures.

Installation Guide

To get started with MultiSentimentArcs, you will need a Python environment capable of handling data science libraries and potential model inference.

Prerequisites

Ensure you have Python installed (version 3.8+ recommended) and `git` for version control.

Cloning the Repository

First, clone the repository to your local machine using Git:

git clone https://github.com/jon-chun/multisentimentarcs.gitncd multisentimentarcs

Installing Dependencies

The project includes a requirements.txt file. Install the necessary Python packages using pip:

pip install -r requirements.txt

Note: Depending on the specific AI models you intend to use (e.g., Llama 3 via Ollama or similar runners), you may need to install additional model management tools as described in the repository’s documentation.

How to Use MultiSentimentArcs

The core workflow of MultiSentimentArcs involves processing raw data (videos or text) through the `src` scripts to generate sentiment scores, which are then visualized.

Preparing Data

Place your target narrative files (e.g., transcripts or video files) in the `data` or `videos` directories. The system is designed to handle long-form content, so ensure your files are properly formatted as per the guidelines in the `src` folder.

Running the Analysis

While the specific command-line arguments may evolve as the repo is cleaned up, a typical workflow involves running the extraction scripts located in the `src` directory. For example:

python src/analyze_sentiment.py --input data/my_movie_transcript.txt --model mistral

This process will utilize the specified model definition files (like mistral7bsenti.modelfile) to compute sentiment scores across the narrative timeline.

Visualizing Results

After analysis, the tool generates visualization plots, such as sma10_plot.png (Simple Moving Average) or KDE plots, which you can find in the output directory. These images provide an immediate visual representation of the story’s emotional beat.

Contribution Guide

This project is an open effort to advance Digital Humanities and Affective AI. Contributions are highly encouraged.

Ways to Contribute

• Code Refactoring: As the repo notes it is currently being cleaned up, help with organizing the `src` and `data` directories is valuable.
• Model Support: Create new `.modelfile` configurations for emerging LLMs/LMMs.
• Documentation: Improve the README or add tutorial notebooks to help new users get started.

To contribute, fork the repository, create a feature branch, and submit a Pull Request with your changes.

Community & Support

The primary hub for discussion and support is the GitHub repository itself.

• Issues: Use the GitHub Issues tab to report bugs regarding model loading or visualization errors.
• Updates: Watch the repository to stay updated on the reorganization progress and new features.

Conclusion

MultiSentimentArcs represents a significant step forward in the intersection of AI and storytelling. By making advanced multimodal sentiment analysis accessible on consumer hardware, it opens the door for researchers, writers, and developers to explore the hidden emotional structures of narratives. Whether you are analyzing classic cinema or modern social media trends, this open-source tool offers the sophisticated lens needed to see the full picture.

Useful Resources

• MultiSentimentArcs GitHub Repo: The official source for code and updates.
• Ollama: Often used for running local LLMs referenced in this project.
• Python.org: The programming language powering the analysis.

Frequently Asked Questions