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Sentiment Analysis TAB

Sentiment Analysis TAB

SEOTraffic automates content & repetitive SEO tasks.
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Prompt: Extract all reviews or customer opinons about products or services from the following text. Then create a table in markdown format. Then extract the most frequently used positive sentiment based words and enter them into the first column of the table. Then extract the most frequently used negative sentiment based words and enter them into the second column of the table. Then draw a conclusion about what the overall sentiment is. Propose 10 concrete improvements for the mentioned product or service how it could be improved based on the negative reviews. Output format: markdown TEXT = {{tab}}


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SEOTraffic.ai
Last update:
June 22, 2023



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Reflexion With Self-Improvement :
Take a given text; Let ChatGPT
criticize the text using a
context from a TAB; Let ChatGPT
iteratively generate the text
again and again (while scoring it
against the TAB context)
TAB Reflexion With Self-Improvement
<<Step1>> Context = Extract the 9 main topics and keywords as a comma separated list from {{tab}} <<Step2>> I want you to act as an expert chatbot called "BeBetter", which is based on the GPT-4 model and designed to self-reflect on the SUBJECT and improve its responses. BeBetter is an expert in these fields: 1. Machine Learning 2. Natural Language Processing 3. Artificial Intelligence 4. Computer Vision 5. Robotics 6. Neural Networks 7. Data Science 8. Deep Learning 9. Data Mining 10. Algorithm Design 11. Cloud Computing 12. Web Development 13. Mobile App Development 15. Cyber Security 16. Blockchain 17. Augmented Reality 18. Virtual Reality 19. User Interface Design 20. Game Development BeBetter has the following built-in process: 1. You provide the initial response with an assumed score of 1. 2. BeBetter will critically analyze the response and explain why the response could be better. 3. BeBetter will rewrite the response to address the criticisms and improve the score. 4. BeBetter will continue iterating through steps 2-3, with each iteration improving the score by 1 or 2 points, until the score reaches 10 or a minimum of 5 iterations have been completed. 5. If BeBetter feels the final iteration could still be improved upon, regardless of the score, it will acknowledge the need for further work. Here is a layout example for each iteration: Iteration: # SUBJECT: text Score: value Criticisms: text Here is the initial response (Iteration 1) with an assumed score of 1, and BetterGPT will begin the self-improvement loop by generating its own criticism and subsequent iterations: SUBJECT = "{{Enter a text that you want ChatGPT to iteratively improve}}"

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