# Tweet Tone Detector
The Tweet Tone Detector is an AI-powered tool designed to analyze the tone and sentiment of tweets. It evaluates the tweet's content and classifies it into various tone categories such as Positive, Negative, Neutral, Humorous, Sarcastic, Enthusiastic, Angry, or Informative. Additionally, it identifies the overall sentiment as Positive, Negative, or Neutral. The agent provides clear explanations for its classifications, highlighting key words, phrases, emoticons, and other elements that influenced its decision, thereby offering insights into how users express themselves on social media.
# Getting Started
To get started, make sure you have cloned the Tweet Tone Detector. Follow Create from Template to clone and configure your AI agent.
# Configuration Instructions
# Knowledge
You can upload .pdf or .txt files to provide the agent with tailored context that meets your specific requirements.
# User Inputs
| Field title | Description | Instruction |
|---|---|---|
| Tweet Content | The text of the tweet you wish to analyze. | Input should be the full content of a tweet, ideally between 1 to 280 characters in length. |
# AI Models
The default model is Llama 3.1 8B Instruct, but you can explore other supported AI models as well. If needed, refine your prompts for more tailored outputs.
TIP
For more detailed instructions on configuring an AI agent, please refer to Configure AI Agent
# Sample Inputs and Output
# Sample Inputs
- Tweet Content: "I absolutely love the new design of the app! It's so user-friendly and colorful."
# Sample Output
Tone: Positive
Sentiment: Positive
Explanation: The use of "absolutely love," "user-friendly," and "colorful" are strong indicators of enthusiasm and positivity throughout the tweet.
# Tips for Effective Configuration
- Be Clear and Concise: The more straightforward and specific your input is, the better the AI will understand the context of the tweet.
- Use Full Sentences: Complete statements allow the AI to better gauge the tone and sentiment compared to fragmented text.
- Consider Context: Tweets can have different meanings based on context; providing background information can enhance analysis quality.
- Experiment with Different Tweets: Test various styles of tweets to better understand how the agent categorizes tone and sentiment.
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