About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – AI Models
- Language – English
- Category – Natural Language Processing
- Prompt Title – Sentiment Analysis AI Prompt for Social Media Monitoring
Prompt Details
This prompt is designed for dynamic sentiment analysis of social media content related to AI models. It aims to be adaptable across various AI platforms and NLP models, providing detailed instructions and parameters for optimal performance.
**Prompt Structure:**
“`
Analyze the sentiment of the following social media text related to AI models:
{social_media_text}
Considering the context of the AI models niche, classify the sentiment as one of the following:
* **Strongly Positive:** Expressing strong enthusiasm, praise, or excitement about AI models. Examples include endorsements, successful applications, or breakthroughs.
* **Positive:** Expressing general approval, optimism, or interest in AI models. Examples include acknowledging benefits, sharing positive news, or expressing curiosity.
* **Neutral:** Expressing objective information, factual statements, or news about AI models without expressing any clear positive or negative sentiment. Examples include announcements, reports, or technical discussions.
* **Negative:** Expressing general disapproval, skepticism, or concern about AI models. Examples include highlighting limitations, expressing doubts about potential, or sharing negative experiences.
* **Strongly Negative:** Expressing strong criticism, condemnation, or fear about AI models. Examples include warnings about risks, reports of failures, or expressions of outrage.
Provide the following output in a structured JSON format:
“`json
{
“text”: “{original_social_media_text}”,
“sentiment”: “{classified_sentiment}”,
“confidence_score”: {score_between_0_and_1},
“relevant_keywords”: [“list”, “of”, “relevant”, “keywords”],
“detected_entities”: [“list”, “of”, “detected”, “entities”, “related”, “to”, “AI”, “models”],
“explanation”: “{brief_explanation_of_the_classification}”
}
“`
**Additional Instructions and Parameters:**
* **Contextual Awareness:** The prompt should consider the specific nuances of the AI models niche. For instance, “bias” within this context likely carries a negative connotation, while “innovation” is usually positive. Be mindful of industry-specific jargon and trends.
* **Irony and Sarcasm Detection:** Implement mechanisms to detect irony and sarcasm, as these can significantly impact sentiment analysis. Consider using contextual clues and analyzing emojis/emoticons.
* **Emphasis on Specific Aspects:** Optionally, the prompt can be extended to focus on specific aspects of AI models. For example, you can add the following instruction: “Focus your analysis on the sentiment expressed towards the ethical implications of {specific_AI_model}.”
* **Handling Negation:** Ensure the model correctly interprets negations like “not bad” (positive) and “not good” (negative).
* **Emoji and Emoticon Interpretation:** Integrate interpretation of emojis and emoticons into the sentiment analysis.
* **Handling Multiple Sentiments:** If the text expresses multiple sentiments, identify the dominant sentiment and explain the different sentiments present.
* **Language Specificity:** If the social media text is not in English, specify the language in the prompt. For example, “Analyze the sentiment of the following French social media text…”
* **Data Bias Mitigation:** Implement strategies to mitigate potential biases in the training data, ensuring fair and accurate sentiment classification across different demographics and viewpoints.
**Example Usage:**
“`
Analyze the sentiment of the following social media text related to AI models:
“GPT-4 is mind-blowing! The code generation capabilities are incredible. While I’m still concerned about potential misuse, the potential benefits are undeniable. #AI #GPT4 #Innovation”
… (JSON output as specified above) …
“`
**Expected Output (Example):**
“`json
{
“text”: “GPT-4 is mind-blowing! The code generation capabilities are incredible. While I’m still concerned about potential misuse, the potential benefits are undeniable. #AI #GPT4 #Innovation”,
“sentiment”: “Positive”,
“confidence_score”: 0.85,
“relevant_keywords”: [“GPT-4”, “code generation”, “mind-blowing”, “potential misuse”, “benefits”],
“detected_entities”: [“GPT-4”],
“explanation”: “The text expresses strong enthusiasm (‘mind-blowing’, ‘incredible’) for GPT-4’s code generation abilities. While a concern about misuse is mentioned, the overall tone and the emphasis on ‘undeniable’ benefits lead to a positive classification.”
}
“`
This dynamic prompt framework allows for customization based on specific needs and provides a structured output for easy integration into social media monitoring systems. By following these guidelines, you can leverage the power of AI for accurate and nuanced sentiment analysis in the ever-evolving AI models landscape.