Text Analysis Tools

Analyze your text files to identify sentiment, brands, topics and trends with Speak Ai's text analysis tools.

With the large amounts of text data that businesses process every day, many companies are sitting on large untapped goldmines in the form of unstructured text documents. Using text analysis to break these massive amounts of data into sentences, phrases, keywords and sentiment, can allow you to better understand trends and topics present across all your files – regardless of the source. 

You can try out Speak Ai’s Named Entity Recognition and Sentiment Analysis below. These are just samples of the type of analysis our platform can help you with, though you can explore the system and analyze multiple files at once with our 14-day free trial.

Named Entity Recognition

Sentiment Analysis

Named Entity Recognition

Sentiment Analysis

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What is the difference between text analysis, text mining and text analytics?

To start off, there is no difference between text mining and text analysis which are often used interchangeably to talk about the process of grabbing data through statistical pattern learning. As a result, you can use either to talk about this process, but we’ll be using text analysis for our explanations. 

Now, when it comes to text analysis vs text analytics  the main difference is that text analysis refers to qualitative results while text analytics is the process of identifying trends and stats – aka quantitative results from your text data. 

When it comes to machine learning, software that identifies important information within your data such as names, sentiment and brands are conducting text analysis while software that identifies patterns and trends related to the text data are conducting text analytics. 

Tl;dr Text analysis = decoding human language; Text analytics = finding patterns and trends that are statistically significant

Why you should integrate text analysis into your workflow?

The power of machine learning and AI is the ability to compile and analyze massive amounts of data in a fraction of the time that’s possible for a human. 

As a result, you are able to experiment and uncover important insights at a much quicker rate which comes with a large number of benefits. 

Using AI for text analysis is scalable

Using an AI text analysis tool allows you to greatly increase the amount of information capture and analysis that can be done to speed up insight extraction. With many systems this is purely limited to parsing through text documents such as emails, customer chats, support tickets and surveys to name a few. However, with the Speak platform, you are able to analyze your text data across audio, video and text files due to our in-built transcription and deep search.  

Create custom data sets and analysis

If you work with industry-specific vocabulary or are looking to identify uncommon terms or topics, it’s easy to set up our system to do that for you. Simply create custom categories and populate them with the terms you would be looking to identify, and our system will do the rest across all your media files. 

Easily identify key information and repurpose captured content

Finding important information across your media files is made easy with a good text analysis tool. This allows businesses or creators to use their media library as an idea safe, with the ability to resurface and repurpose previously captured information. 

What are some use cases of text analysis and natural language processing (NLP)?

Adding text analysis and NLP to your technology stack can be an incredible boost to the business intelligence and data analysis work done for your business. Whether it’s through APIs or a user-friendly software, it’s possible to build out applications of this across many different industries including research, healthcare, filmmaking, retail and SAAS to name a few. 

Some examples of text analysis being applied include: 

Conducting qualitative research

Qualitative researchers can use text analysis to identify important keywords, topics and trends based on their interviews. With line-by-line sentiment analysis as well, it’s possible to code every project accordingly and end up with a final result that is easy to extract insights from.

Voice of customer

Identifying how customers feel about your product as well as gaining a deeper understanding of how they interact with your support team is an integral business function. With customer success growing as its own discipline, practitioners are looking for ways to better understand all the language data that their teams have to work with. 

Knowledge management

An uncommon but equally important use case of text analysis and NLP can be for knowledge management and recall. This is particularly true among organizations or individuals who parse through hundreds of hours of transcripts, interviews and other language data to find relevant information. With a system like Speak, you can easily find mentions of topics and keywords as well as the specific parts of different files that they appear in. This is all achieved through text analysis and NLP, with a bit of engineering magic to boot. 

Our customers love us

I had 10 one-hour interviews that I needed to transcribe and analyze. Speak helped with that process immensely. Wishing you all the luck. I seriously think you have a winning product here.
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Karen Shulman Dupuis
Coach at Centre for Social Innovation
As a person who spends hours per day brainstorming out loud I never had the ability to make sense of all of my thoughts. Speak Ai had the ability to synthesize hours of audio into useful insights.
Justin Finkelstein
Justin Finkelstein
Citi Technology Innovation Center, Founding Member
“This is super cool. I can definitely see the value in what you have built. We look forward to continuing to work with you and accessing this powerful technology.”

Ashley Conyngham
Ashley Conyngham
Director, Marketing & Communications, LEDC

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