Text mining consist of extracting knowledge from various textual data.
If you are already aware of the importance and value of business intelligence to fostering and accelerating the growth of your business or organization, you have certain processes in place to ensure that no data that is useful is missed. The most accurate live data, statistics, correlations and transpositions are all there to serve your business intelligence needs, or seems to be. How would you feel, however, if you found out that you are missing out on up to 80 per cent of all usable data? Only a faction of all data is what one would call numeric, or quantitative. The rest is images, videos, and texts. Given the millions of pages of text created each and every day, including discussions about your product, your company, your competition and the likes and dislikes of your potential customers, text mining, also known as text analytics, truly comes to the fore as your business intelligence tool of choice.
Why text mining?
When working with numbers, it is usually much simpler to create automations and conduct analysis of the data at hand. With text mining, you have to try harder and go deeper, but in the end get new information that was not originally quantitative or even considered quantifiable. Thus by supplementing your data mining in the more traditional sense of numeric values by the deeper business intelligence of text analytics, you take a step forward that many of your competitors would not take.
How does text analytics work?
First of all, you would need to make sure to have a system in place that ensures that the data you take into consideration for your text mining is actually relevant to your business. Your information retrieval thus needs to be state of the art. The next step, natural language processing, or NLP for short, discerns the structures of speech into data that then can be quantified - for example, the correlation of evaluative elements such as “good” or “great” or “disgusting” with the names of products or companies.
The actual analysis of these correlations is preceded by the extraction of these elements from the NLP-processed text, followed by the more traditional data mining stage where the textual elements have already become quantitative data for your purposes.
What are the limitations?
Only a decade ago, NLP systems were crude and unable to discern the majority of text mining elements needed to obtain quality business intelligence. Today, all of the stages of text analytics - the information retrieval, the natural language processing, the data extraction, and the data mining - are much more advanced.
Thus while you will not be able to get the full scope of this qualitative data into a quantitative and thus usable form, you will go a long way as opposed to those relying only on the originally numeric data. Our expert team would be happy to help you make a head start on your text mining business intelligence today! Get in touch and we will answer any questions you may have.