Text analysis, powered by machine learning, continues to shape the business environment.
There’s so much data nowadays that it’s downright impossible for humans to analyze and process this information manually.
That’s why machine learning technology, like keyword extractors, is now an essential part of business operations. They automate repetitive tasks, save time, and deliver instant insights.
A keyword extractor is a machine learning tool that automatically extracts keywords, or important information, from data.
Companies receive endless amounts of data that holds key information about a brand, product or service. It’s found in emails, social media posts, online reviews, survey results, and more.
Keyword extractors can automatically extract this data – in the form of single or compound words, and even sentences – to help businesses understand what their customers are talking about, how they’re talking about topics, or purely to extract important information that they need to upload to a database, such as company names, phone numbers, locations, and emails.
Let’s take a look at an example. Videoconferencing software Zoom is receiving a lot of attention at the moment, good and bad, in the news, on social media, and in online reviews. Now, let’s say they want to hone in on the topics that customers are mentioning most often.
Instead of submitting their teams to countless hours of labor-intensive data processing, they could connect a keyword extractor to their customer service helpdesk, automatically summarize their data and identify what customers are talking about, and even what they like or dislike about the product, giving them quick insights on the spot.
For example, we used a word cloud generator (the simplest form of keyword extraction) to extract keywords from online reviews about Zoom, and could immediately see that customers often mention Features (perhaps Screensharing), Time, Issues (perhaps Audio and Video Quality), and Ease of Use.
While word clouds, also known as tag clouds, allow you to do simple keyword extraction, there are more advanced text analysis tools that deliver more accurate insights.
You can use open-source libraries for Python and R, for example, to create your keyword extraction model or use online software (SaaS) solutions that simplify the process of building your custom keyword extractors.
While open-source libraries are highly versatile, you’ll need to know how to code to use them. On the other hand, SaaS solutions offer low code, no code models, allowing anyone to get started with keyword extraction right away.
Here, at MonkeyLearn, we offer powerful text analysis tools that can be tailored to your needs. You can use our ready-to-use extraction models or build your own custom keyword extractor.
Also, you can leverage MonkeyLearn’s API and connect your data, or use one of our key integrations like Zapier, Twitter, Google Sheets, or Zendesk.
We recommend building your own custom keyword extractor, using tags that are relevant to your business for higher accuracy.
To create your own custom keyword extractor, you’ll need to sign up to MonkeyLearn for free then follow these simple steps:
In MonkeyLearn’s dashboard, click on ‘Create Model’. You will see two options: Classifier and Extractor. Click on ‘Create Extractor’.
MonkeyLearn gives you the option to upload a CSV or Excel file with your text data or import your data directly from an app such as Twitter, Zendesk, Freshdesk, etc. For this example, we are using online reviews about Zoom, which we saved as a CSV file.
Select the columns you want to use to train your extractor.
Based on what you need and what you’re looking for, create tags so the keyword extractor can use them as buckets to sort data. For example, here we’ve used the tags Ease of Use, Issues, and Features to extract information from Zoom reviews.
The key to a highly accurate and powerful extractor is thorough training. To train your model, you need to sort words/phrases and associate them to the tags you created.
This way, your model will learn from the connections you make, gain confidence, and begin to make predictions on its own. The minimum number of texts you need to tag is 15, but we recommend that you go well above this number so the model can reach the level of accuracy and confidence you require.
After naming your model, you’ll need to test it to see how it fares on its own. If, after testing your model, it’s not extracting the correct information, you can continue training your model until it reaches a higher level of accuracy.
Once you are satisfied with your model, you can put it to use by pasting more text, uploading a batch of data, connecting it to your tools using the API, or using one of the available integrations.
In a matter of seconds, your keyword extractor will be up and running and, most importantly, delivering instant insights.
The automated process of keyword extraction helps find value in text data. You’ve got a bunch of emails that you want to process? Upload your text data to your custom-made keyword extractor and learn which topics customers mention most often.
Why process data manually when machines can get the job done in a matter of seconds! Put your social media interactions, survey results, emails, and other customer-fed textual data to use with a keyword extractor, and see for yourself the insights you can gain and the processes you can automate.
Give it a try with MonkeyLearn by creating a free account and get hands-on experience on how to use keyword extractors.
April 3rd, 2020