Topic Extraction API, also called Entity Extraction or Content Taxonomy, uses natural language processing to identify the main ideas and concepts in a text and group them into meaningful topics.It then returns a list of topics with their associated keywords or phrases.
This technology can analyze different types of text data, such as articles, social media posts, customer reviews and others. The Topic Extraction API has many uses, including organizing content, gauging emotions, identifying trends, and optimizing search engines.
It's important to highlight that the Topic Extraction API is immediately available for use, as opposed to Custom Text Classification, which necessitates a dataset prior to implementation.
For users seeking a cost-effective engine, opting for an open-source model is the recommended choice. Here is the list of the best Entity Extraction Open Source Models:
Gensim is a prevalent open-source tool in Python for topic modeling. It implements various efficient algorithms for modeling topics, like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). Further, it features a user-friendly interface that simplifies training and using topic models.
Scikit-learn is a Python library for machine learning that provides different topic modelling methods, including Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorisation (NMF), and Latent Semantic Analysis (LSA).
BERTopic is a method for extracting topics that uses BERT embeddings to enhance the process. It is constructed on the Hugging Face Transformers library.
Top2Vec is an algorithm that combines topic modeling and document embeddings. It's designed to discover topics in large document collections.
MALLET is a Java program that assists in processing language for things such as topic modeling. You can use it for different text tasks because it can implement a variety of machine learning algorithms, including Latent Dirichlet Allocation (LDA), which is useful for topic modeling.
Tomotopy is a topic modeling library for Python that supports various algorithms, including Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), and more. It provides an easy-to-use interface for topic modeling tasks.
BTM is a technique for discovering topics in short texts. It models biterms, which are when two words appear together in a short context. This method is especially helpful for short documents.
While open source models offer many advantages, they also have potential drawbacks and challenges. Here are some cons of using open source models:
Given the potential costs and challenges related to open-source models, one cost-effective solution is to use APIs. Eden AI smoothens the incorporation and implementation of AI technologies with its API, connecting to multiple AI engines.
Eden AI presents a broad range of AI APIs on its platform, customized to suit your specific needs and financial limitations. These technologies include data parsing, language identification, sentiment analysis, logo recognition, question answering, data anonymization, speech recognition, and numerous other capabilities.
To get started, we offer free $10 credits for you to explore our APIs.
Our standardized API enables you to integrate Topic Extraction APIs into your system with ease by utilizing various providers on Eden AI. Here is the list (in alphabetical order):
Google Cloud uses advanced machine learning algorithms to extract important topics and details from text data. The API can process different kinds of documents, like web pages, articles, and social media posts. Google's solution can handle large amounts of data and delivers accurate results in multiple languages.
IBM's Entity Extraction applies machine learning algorithms and NLP techniques to identify significant ideas, objects, and emotions in given text. Customers can use IBM's expertise to handle vast amounts of data and take advantage of multilingual assistance in examining text in various languages.
OpenAI's technology relies on the advanced GPT-3.5 system to achieve high precision and trustworthiness by understanding the input's context. By receiving extensive instruction on substantial datasets, OpenAI's Topic Extraction API assures relevant outcomes, including those involving elaborate and nuanced text.
Tenstorrent's solution uses advanced deep learning methods to accurately identify and categorize topics, resulting in more significant and useful insights. The Tenstorrent Topic Extraction API enables users to easily understand the crucial themes within a given document or dataset, keeping track of trends and changes over time.
Furthermore, the solution provided by Tenstorrent allows for customization at a significant level, enabling users to amend the API's parameters to fit their individual needs.
Eden AI offers a user-friendly platform for evaluating pricing information from diverse API providers and monitoring price changes over time. As a result, keeping up-to-date with the latest pricing is crucial. The pricing chart below outlines the rates for smaller quantities for November 2023, as well as you can get discounts for potentially large volumes.
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The Eden AI team can help you with your Topic Extraction integration project. This can be done by :