Text classification APIs help you sort data into predefined categories. By using AI-powered tools to detect topics, sentiment, intent, language, and urgency in unstructured text, companies can automate daily tasks and gain insights to make better business decisions.
Wondering how to analyze those open-ended NPS responses? Give this topic classifier a go and see how it automatically tags feedback!
You can choose between open-source and SaaS APIs to connect text classification models to your apps. Open-source libraries can help developers build flexible and highly customized models, but take time to set up .
SaaS tools, on the other hand, are much easier and faster to implement, while involving fewer resources and technical expertise. MonkeyLearn is a machine learning platform that offers various plans to suit every business's needs, complete with a powerful suite of text classification models and APIs.
Read on to discover some of the best open-source and SaaS APIs you can use for classifying text.
Most of these tools offer trial versions or pre-trained models, so you can try them out to see if they fit your business model:
MonkeyLearn is a user-friendly machine learning platform that lets you dive into text classification right away using pre-trained models, like this sentiment classifier. You can also build your own customized solutions for more accurate insights.
Custom models are great if you need to increase accuracy, especially if you deal with domain-specific data. In just a few steps (no coding or machine learning skills needed), you can train a model using your own criteria to automatically detect topics, sentiment, and intent.
You can manage your text classification models through the MonkeyLearn API, which is easy to use and available in all major programming languages. Or, you can access integrations and connect your data to everyday apps like Google Sheets, Zapier, and Zendesk.
Google Cloud NLP is a suite of text analysis tools to help you find insights in unstructured data.
Using the Natural Language API, you can connect to powerful pre-trained models, designed to deliver generic results with high accuracy for sentiment analysis and content classification. The content classification tool, for example, allows you to classify documents into more than 700 categories.
If you need a classification model tailored to a specific use case, you can use AutoML Natural Language, which allows you to build customized solutions using your own pre-defined categories.
Aylien is an AI platform that offers different text analysis solutions for businesses. With the text classification API, you can automatically find topics in documents or websites.
The API applies two taxonomies: one that uses 500 categories to tag news and media content, and another that’s more focused on advertising and allows companies to display online ads in the right places.
IBM Watson is a multi-cloud platform with an array of AI tools to classify business data. With the Natural Language Classifier, developers can build custom classification models to find topics in data. You can train a model in under 15 minutes (no machine learning background required) and easily integrate models into your applications using the API.
Watson also provides a pre-built solution for text analysis called Natural Language Understanding, that you can use to find sentiment, emotions, and categories in text.
Meaning Cloud offers a collection of cloud-based APIs for text analysis. The text classification API comes with a series of predefined categories to automatically sort data (for example, you can classify news content into more than 1300 topics), or you can create custom models using your own categories.
The sentiment analysis API, on the other hand, helps you identify polarity and irony marks in text across different languages. Developers can customize this solution as well, defining their own dictionaries adapted to their domain.
Lexalytics is a modular business intelligence platform, featuring different solutions for text analysis. The Semantria API allows you to perform document categorization and sentiment analysis using Natural Language Processing and machine learning.
The models support text in different languages and are highly customizable: you can easily train them to recognize industry-specific vocabulary and tweak different parameters to obtain better results.
Amazon Comprehend is an NLP service with an array of APIs you can easily integrate into your applications. You’ll find APIs for sentiment analysis, language detection, and a custom classification API, that can help you build text classification models adapted to your business needs. You don’t need any machine learning knowledge or extensive coding skills to create a custom model.
Scikit-learn is a user-friendly machine learning library for Python. Through its well-documented API, you can connect to different classification algorithms and build models for tasks like spam detection, image recognition, and topic classification.
It’s a great library for beginners, and performs well in most use cases.
Boasting a large number of resources and algorithms, NLTK is one of the most famous Python libraries for text analysis, especially among researchers and students looking to get hands-on experience.
You’ll find several APIs that you can use for topic classification and sentiment analysis, as well as labeled datasets that you can use to train them. The major drawback about this library is that it doesn’t handle data at scale.
SpaCy is a Python library for NLP, praised for being fast and having industrial-strength capabilities.
It provides an easy-to-use API that allows you to create classification and sentiment analysis models, using state-of-the-art algorithms for each problem.
TensorFlow is Google’s open-source platform for machine learning and has rapidly become the most popular library, used by companies like Twitter, AirBnb and Uber. With tools and resources that are constantly updated, you can start building powerful deep learning models as you learn.
With the Tensorflow API (available in different programming languages) you can build models to perform advanced text classification tasks.
PyTorch is an open-source machine learning framework based on the Torch library and developed by Facebook. Simple, flexible, and fast, it’s excellent for both developers and researchers who want to train deep learning models.
The PyTorch API makes it simple to create text classification models and offers different features to improve your model’s performance.
Keras is a powerful Python library designed to build deep learning models that can run on top of frameworks like TensorFlow, R, and Theano.
With a collection of easy-to-use and intuitive APIs (plus, extensive documentation), this library is a great place to get started with text classification and obtain great results.
Text classification tools help you organize data by topic, sentiment, intent, and more. They allow you to automate time-consuming tasks ‒ like tagging incoming emails and routing customer support tickets ‒ and also provide insight into what your customers think about your business.
Automating daily tasks with text classification is simpler than you think, thanks to open-source libraries and SaaS tools that are easily accessible through APIs.
With a tool like MonkeyLearn, you can create a custom classification model and integrate it into your own apps in just minutes. Ready to try it out? Request a demo from MonkeyLearn and start classifying your data right away.
May 28th, 2020