swarmauri_tool_sentimentanalysis 0.11.0.dev1

Transformer-based sentiment analysis tool for Swarmauri with compact label-only dictionary output.

Swarmauri Tool Sentiment Analysis

swarmauri_tool_sentimentanalysis is the Swarmauri sentiment-analysis tool built on Hugging Face transformers. It wraps the pipeline("sentiment-analysis") surface and returns a simple dictionary with a single sentiment label for the input text.

Why Use Swarmauri Tool Sentiment Analysis

• Add fast text sentiment classification to agent and tool-calling workflows.
• Return a compact structured output that is easy to route, log, or attach to larger decisions.
• Use transformer-based sentiment inference without building a custom model wrapper.
• Pair sentiment labels with entity extraction, parsing, or downstream automation in Swarmauri pipelines.

FAQ

What does this tool return?
A dictionary in the shape {"sentiment": "<LABEL>"}.

What labels should I expect?
Labels depend on the underlying Hugging Face pipeline model. In practice the current tests allow POSITIVE, NEGATIVE, or NEUTRAL.

Does it download a model on first use?
Yes. The underlying transformers pipeline downloads model assets if they are not already cached.

Can I use it inside tool-calling or orchestration workflows?
Yes. It is packaged as a Swarmauri ToolBase component.

Features

• Transformer-backed sentiment analysis via Hugging Face pipelines.
• Swarmauri ToolBase integration with a single text parameter.
• Returns a compact dictionary response suitable for automation and routing.
• Works for quick sentiment checks in text review, feedback analysis, and content classification workflows.
• Supports Python 3.10, 3.11, 3.12, 3.13, and 3.14.

Installation

uv add swarmauri_tool_sentimentanalysis

pip install swarmauri_tool_sentimentanalysis

Usage

from swarmauri_tool_sentimentanalysis import SentimentAnalysisTool

tool = SentimentAnalysisTool()
result = tool("I love this product!")
print(result)

Examples

Analyze customer feedback

from swarmauri_tool_sentimentanalysis import SentimentAnalysisTool

tool = SentimentAnalysisTool()

print(tool("I love this product!"))
print(tool("I hate this product!"))
print(tool("This product is okay."))

Use sentiment labels for routing

from swarmauri_tool_sentimentanalysis import SentimentAnalysisTool

tool = SentimentAnalysisTool()
sentiment = tool("The latest release is disappointing.")["sentiment"]

if sentiment == "NEGATIVE":
    print("Escalate for review")

Related Packages

• swarmauri_tool_entityrecognition
• swarmauri_parser_textblob
• swarmauri_parser_entityrecognition
• swarmauri_parser_bertembedding

Swarmauri Foundations

• swarmauri
• swarmauri_core
• swarmauri_base
• swarmauri_standard

More Documentation

• Hugging Face text classification task guide
• Transformers pipeline documentation

Best Practices

• Cache Hugging Face assets in CI or deployment environments to avoid repeated downloads.
• Validate expected labels against the actual model in your deployment, because different models can emit different label vocabularies.
• Use explicit downstream mapping if your application needs stable polarity buckets.
• Pair sentiment output with other tools when you need richer reasoning than a single label.

License

This project is licensed under the Apache-2.0 License.