Text classification models use machine learning, NLP and AI to automatically classify and organize text.
This makes the process of text analysis faster and significantly more efficient.
Interest in this technology is increasing primarily among B2C companies.
Specifically, classifying and organizing text is being used to help B2C brands understand consumer preferences at scale.
Best Text Classification Tools
#1 IBM Watson Natural Language Classifier
IBM Watson Natural Language Classifier is a cloud-based text classification tool that uses machine learning algorithms to classify text.
Watson NLC offers a number of features, including:
- A self-service interface for training and deploying models
- Customizable models
- Integration with IBM Watson services
#2 Google Cloud Natural Language API
Google Cloud Natural Language API is a text classification tool that uses machine learning to classify text.
Google Cloud NLP offers a number of features, including:
- A wide range of pretrained models
- Customizable models
- Integration with Google Cloud Platform services
#3 AWS Comprehend
AWS Comprehend is a cloud-based text classification tool that uses machine learning to classify text.
AWS Comprehend offers a number of features, including:
- Keyphrase extraction
- Sentiment analysis
- Language detection
- Topic modeling
AWS Comprehend is a powerful tool that can be used to quickly and easily analyze large amounts of text.
#4 Azure Text Analytics
Azure Text Analytics is a cloud-based text classification tool that offers an easy-to-use interface for classifying unstructured text.
The service can be used to automatically categorize and extract insights from text data, making it ideal for tasks such as sentiment analysis, entity extraction, and topic modeling.
To get started with Azure Text Analytics, you will need an Azure subscription. You can then create a Text Analytics resource in the Azure portal. Once you have created a resource, you will need to provision a service endpoint and generate an API key.
Once you have an endpoint and key, you can start making requests to the Text Analytics API. The API supports a variety of methods, including sentiment analysis, topic modeling, and entity extraction.
#5 Heat Intelligence
Heat Intelligence is another example of a trending text classification tool. This software analyzes text data to help brands understand what their customers are saying about them online.
Some of the features of Heat Intelligence include:
- Brand monitoring
- Reputation management
- Social listening
- Sentiment analysis
These are just a few of the many text classification tools available on the market today. As interest in this technology continues to grow, we expect to see even more innovation in this space.
How Text Classification Is Used
Text classification is used for a variety of tasks, including:
- Brand monitoring: track and analyze mentions of your brand across the web
- Reputation management: identify and respond to negative sentiment
- Social listening: monitor social media channels for trends, insights, and customer sentiment
- Sentiment analysis: analyze positive, negative, and neutral sentiment in text
- Lead generation: prioritize leads based on engagement
- Customer service: route incoming messages to the right team or individual
- Intent classification: understand what someone is trying to do (e.g., make a purchase, get support, etc.)
To perform text classification, you first need to choose a supervised learning algorithm. This is an algorithm that will learn from training data that has been labeled with the correct classes.
There are many different algorithms that can be used for text classification, but some of the most popular include Naive Bayes, Support Vector Machines (SVMs), and Linear Regression.
Once you’ve selected an algorithm, you need to train it on your training data. This is where the labels come in.
The training data is a set of examples that have been labeled with the correct class. For each example, the algorithm will learn what features are associated with that class.
For instance, if you’re trying to classify emails as spam or not spam, the algorithm will learn to look for features like “free trial” and “pills” that are often associated with spam emails.
After the algorithm has been trained, you can then use it to predict the class of new examples. This is usually done by feeding in a new piece of text and having the algorithm output a label.
Text classification is a powerful tool that can be used for a variety of tasks. With the right algorithm, you can train it to accurately classify text.
What’s Next for Text Classification Tools
Text classification models are part of the Text Analytics meta trend.
Text analytics is a method of identifying patterns and trends from large amounts of text-based data.
Social media analytics is currently the main use case for text analytics software.
However, the technology is increasingly used for internal use. Specifically, organizations are starting to use text analytics to understand how employees interact with each other.
This can supposedly help pinpoint inefficiencies and points of friction within the organization.
All of this is why the text analytics tools market is forecasted to triple in size over the next three years.
Getting started with text classification
FAQs – Text Classification Tools
What is text classification?
Text classification is the task of assigning a class label to a piece of text. This can be done for a variety of tasks, such as sentiment analysis or spam detection.
What are some common text classification algorithms?
Some common text classification algorithms include Naive Bayes, Support Vector Machines (SVMs), and Linear Regression.
How do I train a text classifier?
To train a text classifier, you need to choose an algorithm and then train it on labeled training data. The training data is a set of examples that have been labeled with the correct class.
For each example, the algorithm will learn what features are associated with that class. Once the algorithm has
Conclusion – Text Classification Tools
In short, text classification is a rapidly growing field with a lot of potential.
We can expect to see more innovation in this space in the years to come.
If you’re looking for a text classification tool, we recommend checking out our list of the best text classification tools for 2023.
This list includes both paid and free options, so you can find the right tool for your needs.