Quanovo | Data Technologies | Liverpool

Text Mining

Text Mining


Text Mining is the process of combing through large amounts of digitized text in an effort to find useful information that may be hiding within that text. This type of analytics is about finding unseen connections and patterns in large amounts of text that would otherwise take a long time to consume manually and would likely therefore remain unprocessed and underutilised.

Using Text Mining techniques can be of great value to a business to identify opourtunities and trends that might be developing in both the consumer and business to business space.

It is usually the case that most businesses turn away from text based data due to the complexity involved with manually turning that data into information that can be used in an actionable way. However, with modern Information Retrieval and Natural Language Processing techniques it is possible to automate this process to easily gain access to the valuable information residing in textual sources.




Reveal Insight

Your business has probably collected tons of raw text data in the past either from customer surveys, maintenance logs, internal feedback or social media posts. Using Data Science techniques we can help you create a high performance service that can mine that underutilised text data for nuggets of informational insight.

Track Sentiment

Measuring public opinions and moods can be advantageous to your business even if you are not tracking the sentiment that your customers has towards your customers. Quantifying feelings towards certain market factors can help you gain insight into the direction your industry is heading and allow you to plan accordingly.

Reduce Overload

Having a huge number of customers is obviously desirable for any business. However, listening and acting on their opinions and request can often be time consuming and hazardous. How do you know that the majority of your customers want one thing or another? With text mining it is possible to consolidate all of their voices into one, allowing you to concentrate on the most pressing issues that concern the largest proportion of your customer base.

Competitor Insight

Through Text Mining and Machine Learning techniques it is possible to consume text based reports and web pages produced by your competitors to gain understanding of their strategies and activities. Therefore, giving you and your organisation the competitive advantage of a deep, sector-wide, understanding of emerging strategic trends.




Consume Articles and Reports

It is often the case that news articles, government reports, corporate white papers and other text based documents contain useful information for business needs. A Text Mining solution can allow you to automatically extract this information into a clear visual that is quick to consume.

Discover Threats

Through Natural Language Processing techniques it is possible to identify suspicious activities that can lead to Fraud Prevention and, when applied in that way, locating persons of interest for security services.

Improve Customer Relations

Leveraging the power of text analysis can help your business improve internally. For example, call centre data can be transcribed automatically into text and then processed by a Text Mining service to help identify optimisations that could be implemented.

Monitor Internal Communication

Your organisation likely produces hundreds, if not thousands, of e-mails every week. Advanced Data Science techniques can help you identify any internal concerns and allowing you make decisions that will have the greatest impact possible on your business operations.

Optimise Recruitment

Recruiting for a new position can be a time-consuming process. Making use of a Text Mining solution to narrow down applications by key words appearing in CVs can help speed up that process.

Put Text Heavy Data to Work

Generally, it is the case that of the data organisations collect and store they utilise mostly the numeric data only. Being able to use your full amount of data can allow you to gain access to the full picture originally captured by the data collection.