Do you wish to know the best AI categorization API? You should check out this article in order to obtain the best tools for you.
Artificial intelligence often refers to the idea that a computer is capable of reasoning like a human, or can learn from previous experiences. Artificially intelligent bots are prescriptive and predictive, inherently existing at the intersection of business process automation, artificial intelligence, machine learning, and big data.
Nowadays, many companies are implementing artificial intelligence (AI) in their business due to the fact that it has a lot of advantages. One of them it takes risks instead of humans, reduces human error, makes fast decisions and it is available all day.
If you want to incorporate AI technology in your business, you should try it out with an API because it gives exact data from multiple sources and allows you to categorize all of the data in real-time, an application programming interface (API) is crucial. This will aid in the creation of a comprehensive profile for each prospect, allowing you to anticipate their activities and respond properly.
For this, you’ll need software that can provide you with the API. However, there is a lot of software available on the internet, not all of it produces the same outcomes. As a result, we’ve compiled a list of the best AI categorization APIs:
1-Klazify
Klazify is the most accurate API since it collects data via an API and allows you to categorize data by URL, email address, or domain. Logos, business information, location, market category, security, apps, and other features are all included in the data.
Two of the most accurate technologies employed in this application are Natural Language Processing (NLP) and a Machine Learning Engine. It also adheres to the IAB V2 standard, which classifies and assesses web material into 385+ topic groupings. JSON, Python, and PHP, to name a few, are all supported.
2-MonkeyLearn
MonkeyLearn is a machine learning tool for text analysis. It gives our users the ability to extract useful information from unstructured text. Topics or attitudes expressed in texts such as tweets, chats, reviews, articles, and more can be recognized.
Writing analysis models can be used to automatically tag your text. Select from a library of established models or use machine learning to develop your own custom classifiers and extractors. Classifiers are used to divide the text into categories. Examples include sentiment, topic, aspects, intent, and priority.
3- Google Cloud Natural Language API
Google Cloud Natural Language API is a service that uses machine learning models to analyze text. The API provides powerful sentiment analysis, entity recognition, syntax analysis, and text classification functionality using simple REST calls.
More than simply returning whether a statement is positive, negative, or neutral, the API can recognize key phrases and entities and classify them according to how they are used in context.
Also published on Medium.