MMSA

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MMSA is an open-source Data & Analytics tool that provides a unified framework for Multimodal Sentiment Analysis. It supports 15 MSA models and 3 datasets, offering both Python APIs and command-line tools for ease of use.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is MMSA?

MMSA is a comprehensive, open-source framework designed for Multimodal Sentiment Analysis (MSA). It allows users to train, test, and compare various MSA models within a single, unified environment. The framework supports 15 different MSA models, including recent advancements, and integrates with three key MSA datasets: MOSI, MOSEI, and CH-SIMS. MMSA is highly accessible, providing both Python APIs for programmatic integration and command-line tools for quick experimentation and deployment. Users can also experiment with fully customized multimodal features using the MMSA-FET toolkit. The project is packaged for easy installation via PyPI, making it straightforward to get started with sentiment analysis tasks.

Best used for

Ideal for researchers and developers who need to conduct Multimodal Sentiment Analysis, compare different MSA models, and experiment with custom multimodal features. Especially valuable for academic research and development in the field of sentiment analysis.

Common actions

analyze sentiment

compare models

process multimodal data

extract features

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Capabilities

Key features

Train multiple MSA models
Test multiple MSA models
Compare multiple MSA models
Supports 15 MSA models
Supports 3 MSA datasets
Python APIs available
Command-line tools

Target Audience

data scientistsmachine learning engineersai researchersstudents

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What datasets does MMSA support for sentiment analysis?

MMSA currently supports three primary Multimodal Sentiment Analysis datasets: MOSI, MOSEI, and CH-SIMS. These datasets provide the necessary data for training and evaluating the various MSA models integrated within the framework.

How can I use MMSA in my Python projects?

MMSA can be easily integrated into Python projects by installing it via pip. Once installed, you can import the MMSA_run function and use it to train, test, or tune different MSA models with custom configurations and datasets.

Can I customize the multimodal features used by MMSA?

Yes, MMSA allows for experimentation with fully customized multimodal features. You can use the MMSA-FET toolkit to extract and integrate your own features, providing flexibility for advanced research and specific use cases.

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Also listed in

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AI Agents & Automation › AI Frameworks & Infra Customer Support & CX › Customer Feedback Coding & Development › Open Source & Models

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