A lot of actionable insights and useful feedback can be found in the raw text given by customers or employees in surveys, social media posts, emails, and communication logs. The information there can help you improve your offerings and boost your brand’s reputation.
But reading through all that text would take far too long, so how do you take advantage of all this feedback efficiently? Through algorithms and automation, text analytics is the key to unlocking those insights.
What Is Text Analytics?
Text analytics is the practice of understanding and extracting the meaning out of raw text. For instance, a business might want to know common patterns and trends in its client survey responses so that it can improve the customer experience. Or it might ask staff members about the workplace environment to find out how it can make the office more productive and pleasant to work in.
In either instance, you want to use software, algorithms, and technologies to get the job done as efficiently and accurately as possible:
- Artificial intelligence
- Machine learning
- Natural language processing
Text analysis is not the same as sentiment analysis. While sentiment analysis is the intended outcome most of the time, text analytics is the procedure to achieve it.
Popular Approaches To Text Analytics
There are many ways to approach the automatic analysis of raw text, and each one has its own unique use cases, benefits, and drawbacks. The choice of which one to use comes down to your individual preferences and needs.
One of the simplest strategies to implement in an algorithm is simple keyword spotting. Scan through the input and fish out specific terms that could point to what the text is talking about.
This approach, however, is notoriously inaccurate and not recommended by professionals working in text analytics for several reasons:
- Multiple meanings: If you’ve ever picked up a thesaurus, you know that most words can have vastly different meanings depending on the context. A basic keyword search would not pick up on that nuance.
- Slang and text speak: Social media posts rarely have a consistent lexicon, with new times rising and falling out of fashion extremely quickly. Having to keep a dictionary of all these terms would be impractical.
- Sarcasm and idioms: Most of the raw text input you receive won’t necessarily be perfect English and can contain potentially misleading terms and phrases.
In other words, word choice simply isn’t a good metric for capturing sentiment. The businesses that do use it only do so for small amounts of data, where human review can act as a supplement.
A common approach actual Text Analytics providers use is the ability to create custom rules for parsing text. These rules can involve regular expressions and if-then statements to generate an easy-to-ready algorithm for processing input.
While more comprehensive than a mere keyword search, rule-based analysis takes a lot of effort to set up initially and maintain over time, especially as new phrases and words suddenly become popular in your industry.
Finally, the ultimate end-goal of most text analytics services is the deployment of machine learning to text analysis. Machine learning differs from the other methods in that it allows the algorithm to improve itself over time with training. A few approaches here are:
- Input categorization: You start by manually categorizing a few example inputs and then feed the information to the algorithm, which will then attempt to build its own rules. The only drawbacks here are the difficulty in training the algorithm properly and the need to update its knowledge with new data regularly.
- Topic detection: The algorithm can learn by itself without much human input in this case. It works by looking at simple words and how often they show up alongside other words in the same review text. It then uses probability and statistics to discover what the topics are, though the results are not always accurate given the complexity of the input text.
- Thematic analysis: In this instance, the algorithm groups together words and phrases into themes and attempts to understand the meaning behind them. It’s an approach that requires little human input, but the time and effort it takes to set up are rather high.
Machine learning algorithms are still being worked on as we speak, and, over time, these strategies will become more robust as the years go on. For now, choosing which text analysis service to use depends on what you need and what risks you are willing to take to achieve this unparalleled efficiency in feedback processing.