Duplicate content can hurt SEO and damage credibility. Learn the impact and get essential tips to keep your content original and effective.

Automate and streamline your text analysis with Tinq.ai Workflows. Effortlessly combine tasks like paraphrasing, sentiment analysis, plagiarism checking, and more in a visual, drag-and-drop interface. Boost productivity and simplify complex NLP processes—all through one endpoint.

Now, you can set a credit threshold and automatically top up your balance whenever it falls below that level. Enjoy uninterrupted plagiarism checking, save time, and get exclusive discounts! Find out how to enable Auto Replenishment and keep your workflow smooth.

Learn how to efficiently extract and analyze web content using Tinq.ai's Article Extractor, both through API and a simple web interface. This guide also shows how to integrate the tool with other Tinq.ai features for a powerful content workflow.

In today's digital age, businesses are generating more data than ever before. The ability to extract insights from this data can be a game-changer for businesses looking to stay ahead of the competition. That's where Tinq.ai's Custom Models come in.

By using Tinq.ai to write more engaging tweets, you can connect with your audience and build a strong social media presence.

In this tutorial, you will learn how to create a simple Discord app and a Bot that reads incoming chats from a server using Python.

Automate brand sentiment analysis on tweets using Tinq.ai, Zapier, and Airtable.

Text classification (or categorization) is one of the most common natural language processing (NLP) tasks. It consists in associating an unstructured text with a tag, which corresponds to a specific class.

NLP is a branch of artificial intelligence that deals particularly with the processing of written language. In short, it is everything that is related to human language and its processing by computer tools.

In this tutorial, we will learn how to build a simple, yet powerful feedback form using Tinq.ai and Airtable. We will use Tinq-ai's sentiment analysis tool to analyze the user's feedback, and classify them as "neutral", "positive" or "negative".