What is Topic Extraction?
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.
Topic Extraction APIs uses cases
You can use Topic Extraction in numerous fields, here are some examples of common use cases:
- Academic Research: analyze large volumes of text data and identify key themes and topics. This information can be used to inform research questions, identify knowledge gaps, and conduct literature reviews.
- Business: analyze customer reviews, feedback, and comments to identify customer needs, preferences, and sentiments. This information can be used to improve products and services, enhance customer satisfaction, and increase customer retention.
- Healthcare: extract relevant information from medical records, such as symptoms, diagnoses, and treatments. This information can be used to identify patterns and trends in patient health outcomes, inform clinical decision-making, and improve patient care.
- News and Media: categorize automatically news articles based on their content and topics, making it easier to manage and search for relevant news stories. It can also help to identify trending topics and news events in real time.
- Law Enforcement: analyze data on various platforms and sources to detect criminal activities, track suspects, and identify potential threats.
- E-commerce: analyze product descriptions, reviews, and feedback to identify popular products, customer preferences, and trends. This information can be used to optimize product listings, improve customer satisfaction, and increase sales.
- Social Media Analysis: identify trending topics, monitor brand reputation, and detect customer sentiment. This information can be used to improve social media engagement strategies.
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.
Best Topic Extraction APIs on the market
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
- IBM Watson
- MeaningCloud
- OpenAI
- Rosette
- TextRazor
- Twinword
1. Cohere
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.
2. Google Cloud - Available on Eden AI
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.
3. IBM Watson - Available on Eden AI
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.
4. MeaningCloud
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.
5. OpenAI - Available on Eden AI
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.
6. Rosette
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.
7. TextRazor
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.
8. Twinword
Twinword's Topic Extraction API uses advanced contextual language understanding to generate human-like 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.
Performance variations of Topic Extraction APIs
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
Performance variations across languages
Topic Extraction APIs perform differently depending on the language of the text and some providers are specialized in specific languages. Different specificities exist
- Region specialties: some Topic Extraction APIs improve their machine learning algorithm to make them accurate for text in specific languages spoken in particular countries or regions. For example, some APIs perform well in English (US, UK, Canada, South Africa, Singapore, Hong Kong, Ghana, Ireland, Australia, India, etc.), while others are specialized in Asian languages (Korean, Japanese, Chinese, etc.).
- Rare language specialty: some Topic Extraction providers care about rare languages and dialects. You can find Topic Extraction APIs that allow you to process text in Gujarati, Marathi, Burmese, Pashto, Zulu, Swahili, etc.
Performance variations according to fields
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, a Topic Extraction API may be more effective in identifying key topics in structured news articles, while another API may be better suited to analyzing informal and diverse topics found in forum discussions or social media post.
Why choose Eden AI to manage your Topic Extraction APIs
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:
- Fallback provider is the ABCs: You need to set up a provider API that is requested if and only if the main Topic Extraction API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
- Performance optimization: After the testing phase, you will be able to build a mapping of providers’ performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best Topic Extraction API.
- Cost - Performance ratio optimization: You can choose the cheapest Text Moderator that performs well for your data.
- Combine multiple AI APIs: This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because Topic Extraction APIs will validate and invalidate each other for each piece of data.
How Eden AI can help you?
Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.
- Centralized and fully monitored billing on Eden AI for all Topic Extraction APIs
- Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
- Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
- The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
- Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.
You can see Eden AI documentation here.
Next step in your project
The Eden AI team can help you with your Topic Extraction integration project. This can be done by :
- Organizing a product demo and a discussion to better understand your needs. You can book a time slot on this link: Contact
- By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
- By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
- Having the possibility to integrate on a third-party platform: we can quickly develop connectors.
Top comments (1)
Great post, indeed! Entity extraction is super useful when you have to gather keywords to define a context. The problem at hand throws various challenges, like having to extract entities from unstructured data. There are various tools to tackle such problems, such as NLP lab by John Snow Labs, which is a free to use no-code platform that provides automatic text annotation, building relations among entities and model training for your use cases, even in the domains of healthcare, finance, law, etc.