What’s in a word? The right text analytics software can tell you! It can pull meaning from any text, giving your brand intel on attached sentiments, audience demographics and much more – all from one wee little word or series of words. But, not all text analytics software tools are created equal . . .
In this article, we will discuss a “must have” list of capabilities, including:
- Manual Rules
- Word Spotting
- Text Categorizing
- Topic Modeling
- Thematic Analysis
- Sentiment Analysis
- Trend Analysis
It’s important to note that text data usage is only increasing, as these stats bear out:
- There are 5 quintillion bytes of data created each day as of this writing.
- On Facebook, there are 510,000 comments posted and 293,000 statuses updated every minute
- 9,281 tweets are sent every second.
That’s a lot of data to capture.
Text analytics software uses advanced AI algorithms to aggregate, organize and cluster together text into semantically similar categories. This text data is extracted from the 2.5 quintillion bytes of data created each day and it’s gathered from social media posts, blogs, articles, research papers, consumer reports, even patents. If it has text attached to it, structured or unstructured, it can be captured and analyzed.
Text analytics software takes brands beyond merely categorizing text. It’s able to derive meaning from text to extract consumer sentiment. And ideally, you’ll want one that offers Next Generation AI with Natural Language Processing (NLP) to capture and analyze this intel with the greatest level of accuracy.
Deciphering consumer and market intelligence requires deep parsing and grammatical analysis of data, while organizing it into semantic segments for visualization. And when it is powered by Next Generation AI, it evolves as time and technology advances. Assuming that technology forms your tool’s base (and it should), let’s explore more requirements for text analytics software selection!
1. Manual Rules
Your text analytics software must offer vigorous support of search indicators and filters. Manual rules help you dial in and get specific results from a larger conversation.
For example, your CPG client wants to add more vegan options, focused on athletes, to their line of products, typing in “vegan health food” is going to return too many results and muddy your research waters. The option to use operators such as AND, OR, AND NOT help to whittle down your results so you can find exactly what you need to inform your client. That’s super basic, but you get the idea. The ability to define your own specific terms is important.
2. Topic Modeling
On Facebook, every minute there are 510,000 comments posted and 293,000 statuses updated. That’s a lot of text to be sorted and analyzed. Topic modeling not only weeds out what isn’t applicable to your search, but it neatly organizes what is into semantic boxes or clusters. For example, a search on breakfast foods may be sorted and categorized by cereal options, egg dishes, on-the-go breakfast ideas, plant-based breakfast, etc. This is a huge time saver for any brand and a ‘must have’ for any text analytics software option you’re considering.
3. Word Spotting
One you’ve sorted out your visualizations, keywords are an excellent way to dig deeper and grab more insights from your search. This is because you can isolate these keywords and see how they connect across multiple categories or themes.
Maybe you notice the keyword ‘plant-based’ is popping up more often and assume it’s connected to diet. Digging deeper into your text analytics software may reveal that it’s used for other conversations, including those of sustainability.
Or perhaps you’re a skin care company and are deciding which category of product to produce next. You might compare popular keywords such as lip gloss, lip stick or lip stain to see which appears more frequently for guidance around messaging. And word choice may be closely tied to a specific audience subset. This is all important to know at the start of your campaign planning.
4. Text Categorization
Text categorization tools process and tag unstructured text, sorting it into categories to extract meaning, and aiding in problem-solving. It is a fundamental task of NLP and can be applied broadly with sentiment analysis, topic labeling and spam detection.
How does it look? No doubt you’ve seen a word cloud before – this is text categorization in action. Text categorization sorts the conversation into categories such as terms, brands, people, things and even hashtags.
Language is difficult. From idioms such as ‘break a leg’ to alternative spellings such as ‘smol’ for small. Your text analytics software should make sense of these as well as any misspellings. It also needs to:
- Differentiate between words and phrases with alternate meanings such as novel, row, or nail.
- Manually filter, tag or utilize advanced search operators to fine-tune results.
- Pinpoint attached sentiments for easier understanding.
Words and phrases will come and go but your tool should be able to keep up with the times – and the slang – as it changes.
6. Sentiment Analysis
Most purchase decisions are made with emotion, which makes understanding sentiment a very valuable ally indeed.
Sentiment analysis tools analyze text for attached emotions. This is critical in deciphering meaning from posts on social media. Someone posting, “The iPhone has never been this good!” could be read as negative without the proper text analytics software. However, if you have the right social analytics tool, you will capture the actual intent behind the words.
7. Thematic Analysis
A thematic analysis is another capability in a robust text analysis software suite. It allows your brand to better understand the voice of the customer (VoC). It takes all the qualitative data and sorts it according to an applied theme. This could be brand attributes, brand issues, interests, professions or even emotional targeting. Applying different themes can answer these questions:
- How do consumers view my products value in comparison to price?
- What are the differences in opinion between Generation Z and Millennials when it comes to vegan food?
- What are some common interests that link my audience? And how can I leverage this for broader reach?
Thematic analysis can also inform you on brand perception, campaign progress, new product ideas – the options are endless.
8. Trend Analysis
There’s nothing a consumer likes talking about more than a new trend. And even when they aren’t directly talking about it, they may be using hashtags or phrases that leave clues.
On TikTok, for example, hashtag challenges are all the rage. But often there isn’t any text but the hashtag itself. Trend analysis locates these word usages and applies a trend score to help you identify words or phrases on their way up, or those losing steam and dwindling away. A brand can use this language to shape and influence trends by getting in on the conversation, garnering more share of voice and winning favor from consumers.
Just remember – there are 9,281 tweets sent every second – and that’s just one data point of many available to your brand online. So, when it comes to text analytics software, you need everything listed above to decipher those data points or you’ll be missing a very valuable part of the puzzle. Get the complete picture of what’s possible by reaching out for a demo!