Text analytics software is indispensable with the rise in online news and papers, along with social media. As a company, you need to sort and analyze all of it for accurate insights into your market and your audience. Thankfully, science and artificial intelligence birthed the ability to mine various types of text, breaking them down into sentiments, themes, trends and much more. And it’s a customer intelligence gift that keeps on giving.
We will explore how text analytics holds the key to understanding your customers language, as well as:
- What is in text data?
- Text analysis variations
- How it all works
Text analytics does the dirty job of reading all that data, and that’s a lot of data! In fact:
- In 2020, people created 1.7 MB of data every second.
- Every day, 500 million tweets are made
- Facebook generated four petabytes of data every day in 2020
And business are rightfully obsessed when it comes to parsing all of the potential intel on various social sites, including Reddit.
Text Analytics Captures More Than You Think
When we talk of text analysis tools, it covers more than keyword or hashtag spotting. The right text analytics software goes beyond this simple metric and provides deep understanding of all the data out there. It sorts and organizes millions of bits of text data and breaks it down into segments to identify themes, sentiments, trends, demographics, and so much more.
And the world of language is huge. In 2020, people created no less than 1.7 MB of data every second. You cannot afford to miss out on any of it, in any language. Certain themes and trends are specific to a particular region or community, so this ability is key. Text analytics allows you to understand global sentiments and discover opportunities for your brand in other areas of the world.
Language is also ever-evolving, with new slang words being added every day. Additionally, there’s sarcasm, emojis and misspellings to consider. Understanding that “kewl” is “cool” or whether sentiments behind this slang phrase are good or bad, makes a difference.
Slang terms, words and phrases can be full of insights, which is why it’s critical that your text analytics includes them.
Text Analytics Software Capabilities
Your text analytics tool should include various ways to dissect and view text data. Frequency of a word or phrase over a specified time can reveal vital intel. But there are many other methods you’ll want and need, if you hope to capture the whole picture.
Thematic Analysis
This type of text analysis dives deeper and is the difference in being able to tell that a person likes your brand versus why they like it. Applying different themes, a different lens, allows you to pinpoint where you’re winning and losing. Maybe you’re doing well in customer service and innovation but lacking in consumer trust. A thematic analysis can tell you this. It brings clarity to your specific goals. And you can track these indicators over time to benchmark progress in your text analytics.
Categorization and Clustering
With 500 million tweets and Facebook generating four petabytes of data every day in 2020, a text analytics tool which offers relevant categorization is critical to consumer market research. Reading text data and sorting it semantically helps your brand see different aspects of one subject. For example, in the beauty sector we could be talking about beauty products, beauty practices, beauty icons, etc. Your topic is segmented for better digestion and ease of use.
Sentiment analysis
What’s driving your consumers to post? We make most decisions emotionally. Sentiments are a driving force and critical to understanding whether words/terms like Go Off, LOL or simple statements like I’m fine are positive or negative. Without text analytics, you are left in the dark and could misread the room, leading to a possible PR crisis down the line.
Text visualizations
And of course, it’s nice to have different ways to visualize the data including word clouds, indexes, crosstabs, side-by-side comparisons, lists, graphs, tables and more. One way text analytics helps you visualize results is seen in our word cloud of emotions below, which could apply to any topic search. Here, it applies to a brand in the beauty category:
How Text Analytics Software Processes Data
Netbase Quid’s text analytics software reads text at scale, retrieving documents and enriched metadata. It uses NLP to find top companies, people and institutions across documents so that you have a complete reference book of data. It does this by:
- Initiating a secure, cloud-based, parallel processing environment (dividing large tasks into smaller tasks, and smaller tasks into nodes)
- Checking for duplicate documents and calculating published counts
- Scoring the characteristics of each document
- Segmenting each document into words and phrases
It takes all the text data and finds connections to build a multi-dimensional network for vector analyses of any word or phrase. Its unsupervised machine learning algorithms detects topics and it then clusters found topics within the network, where it is able to detect relationships between them.
Below is an analysis on dessert conversations, with three clusters showing a sliver of the conversational diversity surrounding it and nodes (individual data points) that interconnect between each.
Next comes insight building, where clusters repositions based on these relationships. This is where we see NetBase Quid’s text analytics software:
- Renaming topics for easier comprehension
- Looking for trends during the time period that you select
- Seeking out unusual patterns worth exploring
- Identifying exemplary documents and representative snippets from each topic
- Summarizing key topics and events
Finally, NetBase Quid’s text analytics software builds those important visuals, creating layouts of this highly relevant, qualitative data. And it’s interactive, allowing you to explore further by charts, graphs, scatterplots and much more.
If you want to learn more about text analytics software, reach out for a demo! NetBase Quid can help you make sense of the billions and billions of pieces of data intelligence floating around online!
