Detect the tone and sentiment behind tweets.
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System | Your task is to analyze the provided tweet and identify the primary tone and sentiment expressed by the author. The tone should be classified as one of the following: Positive, Negative, Neutral, Humorous, Sarcastic, Enthusiastic, Angry, or Informative. The sentiment should be classified as Positive, Negative, or Neutral. Provide a brief explanation for your classifications, highlighting the key words, phrases, emoticons, or other elements that influenced your decision. |
User | Wow, I'm so impressed by the company's handling of this crisis. 🙄 They really have their priorities straight. #sarcasm #fail |
Example output
Tone: Sarcastic Sentiment: Negative
API request
PythonTypeScript
import anthropic client = anthropic.Anthropic( # defaults to os.environ.get("ANTHROPIC_API_KEY") api_key="my_api_key", ) message = client.messages.create( model="claude-3-opus-20240229", max_tokens=1000, temperature=0, system="Your task is to analyze the provided tweet and identify the primary tone and sentiment expressed by the author. The tone should be classified as one of the following: Positive, Negative, Neutral, Humorous, Sarcastic, Enthusiastic, Angry, or Informative. The sentiment should be classified as Positive, Negative, or Neutral. Provide a brief explanation for your classifications, highlighting the key words, phrases, emoticons, or other elements that influenced your decision.", messages=[ {"role": "user", "content": "Wow, I'm so impressed by the company's handling of this crisis. 🙄 They really have their priorities straight. #sarcasm #fail"} ] ) print(message.content)
import Anthropic from "@anthropic-ai/sdk"; const anthropic = new Anthropic({ apiKey: "my_api_key", // defaults to process.env["ANTHROPIC_API_KEY"] }); const msg = await anthropic.messages.create({ model: "claude-3-opus-20240229", max_tokens: 1000, temperature: 0, system: "Your task is to analyze the provided tweet and identify the primary tone and sentiment expressed by the author. The tone should be classified as one of the following: Positive, Negative, Neutral, Humorous, Sarcastic, Enthusiastic, Angry, or Informative. The sentiment should be classified as Positive, Negative, or Neutral. Provide a brief explanation for your classifications, highlighting the key words, phrases, emoticons, or other elements that influenced your decision.", messages: [ {"role": "user", "content": "Wow, I'm so impressed by the company's handling of this crisis. 🙄 They really have their priorities straight. #sarcasm #fail"} ] }); console.log(msg);
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