In today’s video we learn how to take advantage of AI to rank different text from 1 (very negative) to 10 (very positive).
This amazing tool is invaluable for professionals in any field for understanding clients better. By analyzing emails and letters, it can gauge urgency and help prioritize responses. Monitoring social media sentiment reveals public opinion on legal cases, providing crucial insights. In contract reviews, sentiment analysis can flag sections with unusual language, highlighting potential issues that need closer examination. A sentiment analysis tool is highly useful and should always be used alongside a lawyer’s expertise.
The first step is to type or paste the text you want to analyze. Next, click on “Analyze Text,” and in a matter of seconds, you’ll receive your ranking along with an explanation of why that rating was given.
– Let’s build this bot step by step! –
Step 1: Input your data
As usual, we begin by typing our bot’s name and a concise description.
Next, we will include a text area node. This node will create a space in the frontend where users can either type or paste the text they want to get ranked.
Step 2: Processing information
Next, we have added an AI Output node. This node’s purpose is to analyze the text provided in the frontend, give it a score based on our prompt, and explain the meaning of the text and why the specific score was assigned. Now, let’s look at how we’ve configured this node to achieve these results.
First, select or create the appropriated AI settings to your or your client’s needs to optimize the outcome.
Next, let’s move on to prompt creation, a step you might already be familiar with. In the system prompt, we instruct the AI on the task we want it to perform. In our example, we ask it to analyze the overall sentiment, rate it on a scale from 1 to 10, explain the meaning of the text, and how it reached this conclusion. Then, in the dynamic prompt, we specify what we want to analyze by including the “text area variable,” which contains the text provided in the frontend, and the desired format for the response.
To end with this node, we select how we want the AI to be triggered. In this case we have picked a dedicated button that cannot be retriggered, and we have called it “Analise text.” Remember that these are the most suitable settings for our example, but you can adapt it to fit your specific requirements.
Step 3: Displaying the result
Finally we have reached the last node. We add a “text field “node in which we include the “AI Output” variable, which purpose is to display the AI response on the front end.
And that’s it! Now you have learned how to create a bot that can easily rank your text in a 1 to 10 scale.
We hope this video has been useful and inspires you to create your own sentiment analysis bot or integrate this functionality into more complicated bots.
Happy automating with e!
