Topic Extraction API, also known as Entity Extraction or Taxonomy of content, uses natural language processing (NLP) techniques to identify the main ideas and concepts in the text and group them into meaningful topics.
The technology typically takes in a piece of text as input and returns a list of topics along with their associated keywords or phrases. It can be used to analyze various types of text data, including articles, social media posts, customer reviews, and more. Topic Extraction API can be useful in a variety of applications, such as content categorization, sentiment analysis, trend analysis, and search engine optimization.
It’s worth noting that Topic Extraction API can be used instantly, unlike Custom Text Classification which requires a dataset beforehand.
You can use Topic Extraction in numerous fields, here are some examples of common use cases:
These are just a few examples of Topic Extraction APIs uses case. This technology can be used in various fields to extract meaningful insights from unstructured text data.
While comparing Topic Extraction APIs, it is crucial to consider different aspects, among others, cost security and privacy. Topic Extraction experts at Eden AI tested, compared, and used many Topic Extraction APIs of the market. Here are some actors that perform well (in alphabetical order):
Cohere's solution utilizes advanced deep learning techniques to accurately identify and categorize topics, resulting in more meaningful and useful insights. With Cohere's Topic Extraction API, users can easily understand the most significant topics within a given document or dataset, as well as track changes and trends over time. Furthermore, Cohere's solution is highly customizable, allowing users to fine-tune the API's parameters to fit their specific needs.
Google Cloud uses advanced machine learning algorithms to extract relevant topics and entities from text data. The API can handle a wide range of document types, including web pages, articles, and social media posts. Google's solution is highly scalable and can process large amounts of data while also ensuring accurate results in multiple languages.
IBM’s Entity Extraction leverages machine learning algorithms and NLP techniques to accurately identify key concepts, entities, and sentiments within a given text. IBM provides users with the ability to handle large volumes of data and multilingual support to analyze text in multiple languages.
MeaningCloud provides a powerful tool that can perform morphological, syntactic, and semantic analyses of text in several languages. MeaningCloud allows users to adjust the API's behavior to different operating scenarios, formats, and languages. Additionally, the solution can recognize a hierarchy of 200 entity types, including names of people and organizations, and can extract multiword concepts, disambiguate terms, and detect co-occurrences. Furthermore, users can even create their own dictionaries, making the API highly customizable to specific use cases.
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OpenAI's solution is built on the advanced GPT-3.5 architecture and is designed to be highly accurate and reliable by understanding the context of the data input. Trained on vast amounts of data, OpenAI’s Topic Extraction API ensures relevant results even for the most complex and nuanced text.
Rosette's Topic Extraction API offers both cloud-based and on-premise deployments, making it flexible and easily accessible for users. The API is fast, scalable, and comes with industrial-strength support, ensuring reliability and consistency in its results.
Tenstorrent's Topic/Entity Extraction solution harnesses advanced AI and natural language processing techniques to accurately identify and extract key topics and entities from textual data. This enables businesses and developers to gain valuable insights from unstructured text, facilitating the extraction of meaningful information and enhancing the understanding of textual content.
Using millions of Wikipedia pages, the topic tagger can assign relevant categories to content with no additional training on the user's data. This knowledgebase of entity and word category relationships ensures that the tagger has an automatic understanding of thousands of different topics at different levels of abstraction, including constantly evolving changes in language.
Twinword's Topic Extraction API uses advanced contextual language understanding to generate topics even in the absence of a particular word. This makes it a highly flexible and powerful tool that can be tailored to the needs of any business or organization.
For all companies who use Topic Extraction in their software: cost and performance are real concerns. The Topic Extraction market is quite dense and all those providers have their benefits and weaknesses.
Performances of Topic Extraction APIs vary according to the specificity of data used by each AI engine for their model training
Topic Extraction APIs perform differently depending on the language of the text and some providers are specialized in specific languages. Different specificities exist
Some Topic Extraction APIs trained their engine with specific data. This means their performance can vary depending on several factors, such as the length and complexity of the text, and the type of content being analyzed. For example, some Topic Extraction APIs may perform best in identifying key topics in structured news articles, while others may be better suited to analyzing informal and diverse topics found in forum discussions or social media post.
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Topic Extraction tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Logo Detection, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple Topic Extraction engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs:
Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.
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You can see Eden AI documentation here.
The Eden AI team can help you with your Topic Extraction integration project. This can be done by :
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