Be it tweets, online reviews, emails, or surveys, all your information starts to pile up. Even if you have an experienced team to tag and sort this data, it would take them days to read and organize thousands of messages. On top of that, tagging text data is unrewarding and monotonous.
Luckily, online text analysis tools can help your team sort, categorize, and gain deep insights from your text data in a quick and efficient way.
In this post, we’ll explain how text analysis with machine learning is useful for your company and show you how easy it is to get started with online text analysis tools.
Finally, we’ll introduce you to some of the best online text analysis tools, and even show you how to build a custom model to analyze your text.
Text analysis is an automated process that uses machine learning and natural language processing to extract or classify text. It performs analysis on all your feedback in real time, allowing you to structure your data in seconds and obtain interesting insights about your business.
First, you need to teach text analysis tools how to carry out certain tasks by manually training them. It may sound complicated, but no-code tools like MonkeyLearn, make it really easy to get started with text analysis online.
Online text analysis tools can help you improve your processes and overall business.
Instead of asking customer support teams to manually analyze customer feedback, you can analyze it instantly with text analysis tools in a matter of seconds.
Let’s take a look at some of the benefits in more detail:
MonkeyLearn has a number of pre-trained text analysis tools and techniques ready for you to try out.
Text classification is a text analysis technique that organizes texts into categories so that it’s easier to make sense of them.
A sentiment analyzer detects emotions in text, and classifies them as Positive, Negative, or Neutral. For example, a comment that reads “It’s too expensive”, would be tagged as Negative.
Sentiment analysis tools allow you to easily assess your brand’s reputation, whether customers love or hate your products or services, and identify urgent issues.
Topic analysis tools sort texts by topic. For example, you can classify product reviews using tags such as Pricing, Customer Support, Design, and more, and automatically route them to team members best equipped to deal with each topic.
You can even combine this model with a sentiment analysis model to perform aspect-based sentiment analysis, which tells you how customers feel about specific topics.
Try out this NPS survey response classfier.
Text analysis can also detect the language of a text. This is very useful when you have issues from customers in multiple languages and you want to route incoming messages to agents who are able to understand them. Our pre-trained language detector can differentiate between 49 different languages. Give it a try!
An intent classifier can help you understand the intentions of your customers.
Let’s say you receive an email. An intent detection model will analyze the body and the subject of the email, and depending on the words and expressions customers use, machine learning will tag the message accordingly. A client who writes “Let’s arrange a call tomorrow” is clearly Interested, and your text analysis tool will recognize that intent.
This is another form of automated language processing. This text analysis technique will extract specific information that is already in the text. For example, you can examine a batch of product reviews and obtain the main keywords customers use.
If you are looking for the most relevant terms or phrases, then a keyword extractor can help you. When using this model, you’ll get a summary of your texts in list form.
An entity extractor analyzes your text and extracts information, like names, numbers, email addresses, locations, and more, which already appears in your text.
There are many text analysis tools available online, including open-source libraries such as TensorFlow, NLTK, PyTorch, and Scikit-learn. To use these tools, however, you’ll need to know how to code to successfully build a text analysis model.
Ready-to-use online text analysis tools, like MonkeyLearn, are a lot easier to get started with because:
To perform online text analysis on huge amounts of data, you’ll need to sign up to MonkeyLearn for free, then follow the tutorial, below:
To run text analysis on large quantities of data, you can upload an Excel or CSV file.
Once you have accessed the sentiment analysis model, click on ‘Batch’. There, you can upload an Excel or CSV file containing your texts.
Select the rows to examine and voilá! You will be able to download an Excel file with the results.
Integrations are also available, so you can easily connect text analysis models to data in your apps:
If you know how to code, you can use MonkeyLearn’s API to run text analysis models with programming languages such as Python, Ruby, PHP, or Java.
If you are looking for more accuracy when examining your texts or use your own set of tags, then you could build and train a custom model for text analysis. This model will learn from your own data and criteria for extraction or classification. Let’s take a look at how to build a custom model for topic analysis.
Access your dashboard and click on create a model. In this example, we’re going to build a classifier:
In this step, you’ll need to upload some texts as samples to train your model. We’re going to upload a batch of product reviews, for example. You can upload either an Excel or CSV file:
The next thing you need to do is define your tags. You’ll need at least two. Take into account that the more tags you add, the more samples you’ll need to train your model:
Now it’s time to tag your reviews to train your model. The more you tag, the smarter your model will become, and the more accurate it will be at making predictions:
Once you have finished tagging, you’ll see a screen like this one:
Click the ‘test’ button and see how it detects topics by typing your own review in the text box:
If you want to improve the accuracy and confidence of your model, you just need to keep adding and tagging new data samples. You can also correct incorrectly tagged examples within your model. If you’d like to know a bit more about how online text analysis tools work, check out this guide on data analysis.
Thanks to text analysis, your support team no longer has to spend endless hours reading and tagging customer feedback to determine if it’s urgent, what it’s about, and so on.
Text analysis is scalable, fast, and consistent, and will power up your human workforce.
The best part is that there are plenty of text analysis tools available online, and it’s free to get started. Plus, with tools like MonkeyLearn, you won’t need to enter a single line of code.
We’ve mentioned several ways in which online text analysis tools can help your business make the most of your data. Now, it’s over to you. Give text analysis a try and ask for a demo today! Our team will be more than happy to show you how to use MonkeyLearn’s text analysis tools.
August 28th, 2019