The amount of data produced every day is quite staggering, especially with the continuous technological advancements being made. According to Statista, more than 2.89 billion people access Facebook monthly and Youtube alone produces more than 500 hours of video every minute (source).
Needless to say, all this content must be cataloged and monitored for various reasons: user preferences, copyright infringement, and offensive material, to name a few. Without artificial intelligence-driven solutions like text mining, the time it would take to perform these tasks would be longer than the time humans have existed.
What is text mining?
Text mining is the act of taking vast volumes of raw text data and performing an analysis in order to produce structured results. Text mining is a task often delegated to AI-powered mining tools due to the benefits automation and machine learning bring to the mining process.
What is the difference between text analysis, text mining, and text analytics?
Text mining and text analytics use different analysis methods and their results serve different purposes, as text analytics is used to convert text into visual data.
How does text mining improve decision making?
There is no disadvantage to having as much information as possible when making a decision. Text mining produces precise, useful data, which can be used to approach any issue or challenge with an extra set of tools at hand.
Access to valuable business insights
Text mining can help someone go through vast amounts of data and focus on the exact keywords and numbers they need. This gives analysts a better framework to work with and business owners a clearer view of the outcome of their decisions. Business intelligence tools have been used by companies like IBM and Siebel (nowadays Oracle) since the 1970s.
Awareness of potential risks and opportunities
Artificial intelligence is useful for much more than increasing profits. Risk analysis performed with the assistance of text mining tools can help a business notice forthcoming issues before they happen. A lot of sensitive information is transmitted in digital text form, and this fact leaves the gates open for problems to happen, either accidental or man-made.
The financial services industry, for example, is constantly at risk of having its data compromised. Criminal activities like fraud or money laundering, nonetheless, can be monitored by using machine learning text mining techniques. Having access to this information can help those responsible take appropriate action.
Understanding how to increase customer satisfaction
Gaining proper comprehension of your customers’ online opinions can be quite a hassle, considering the amount of information one is expected to receive after a product launch or other such events. Customer experience can be better assessed with the help of text mining techniques such as Natural Language Processing (NLP), which can go through thousands of emails and comments and gather the gist of what they feel.
Why is text mining important?
Research performed automatically by artificial intelligence can drastically hasten any kind of process. Thousands of hours that would otherwise be spent analyzing text can be given better purpose and the faster pace of research benefits society as a whole.
Text mining techniques
Here are key text mining techniques:
- Information extraction
- Information retrieval
- Natural Language Processing
- Cluster analysis
This approach consists of the automatic extraction of information from any unstructured or semi-structured document or source, as long as it is machine-readable. Technological advances permit data extraction from written documents and non-textual sources such as video or audio. These can then be interpreted by artificial intelligence solutions and presented in text form.
Information extraction has been performed by DARPA since the late 80s, but its relevance is growing with the times as the internet grows and more data can be harvested from it. Even Tim Berners-Lee, father of the world wide web, advocates for the future of the web to be a data-driven network, making the whole internet machine-readable. Information extraction will be a prime asset in this very plausible scenario.
While information extraction is meant to provide automatic solutions to text management, information retrieval functions by indexing and classifying large document collections, based on specific words, phrases, or other forms of metadata. The data can then be accessed later at request, the same way someone can look up a word in the dictionary.
Information retrieval systems are extremely convenient and can be used to search for all kinds of content, regardless of complexity. In fact, without them, Google and all other search engines would be an impossibility, as the literal purpose of a search engine is to retrieve information based on keywords.
Natural Language Processing (NLP)
Natural Language Processing is the study of the interaction between human language and machines’ understanding of it. This is a discipline in the field of linguistics as much as it is related to text mining and artificial intelligence. The purpose of this field is to improve how machines can understand the contents of data, including context that would not be perceivable if every word was taken at face value.
Speech recognition software is a major achievement in the field of NLP. Not only does it allow machines to understand human language and speech patterns, but it also allows for the automatic conversion of spoken words into text.
Classification allows for organized and clean access to information by separating data into a set of categories. This technique works alongside “training” information that must be fed to a machine learning algorithm, which in turn will use it to categorize any kind of data it interacts with. The purpose of classification is to polish data to its most useful quality, where it can be reviewed and employed effortlessly by the end-user.
Classification has a very positive impact on our everyday life, as it is the text analysis tool behind email spam filters. Used in tandem with other techniques like NLP it can also perform more complex tasks, which include image recognition and cross-examination of scientific data, a process known as multi-omics data analysis.
This technique searches through a data set looking for similarities and then builds clusters with the results. Information on the different clusters can then be mixed and matched, to observe where their similarities and differences lie.
A cluster analysis on the performance of sportsmen in team sports, for example, can help us discern who performs at a similar skill level. This kind of information can be very important to coaches, as they will know which athletes should train together to get the best out of their training.
Summarization consists of producing a comprehensive summary out of large volumes of data. Even though that might sound quite simple, it is essential to the workings of modern society, as the impracticality of raw data can have a very negative impact on any task it is applied to.
If one were to print the human genome, it would occupy over a hundred volumes and it would take the average human more than their entire lifetime to read them. Thanks to AI-powered summarization, the human genome can be used by epidemiologists and other medical professionals to better exercise their professions.
How does text mining work?
Text mining works by following these steps:
- Gather unstructured data
- Data cleansing
- Text analysis
Step 1: Gather unstructured data
The text analysis process begins with the creation of a document that contains the data to be analyzed. Sources of data may be internal, as is the case when studying databases or spreadsheets, or may come from external sources such as social media and news outlets.
Step 2: Data cleansing
Once data has been gathered, it is likely to have multiple incorrect or incomplete data that could negatively impact text analysis. It is also likely to contain duplicates, as the algorithm gathers information from multiple sources. To ensure reliable outcomes, these errors are removed from the dataset or corrected.
Step 3: Text analysis
If everything is in working order, then the text mining itself can commence. This will be an automatic process, and it won’t be long before the data can be put to use. The results, however, will be dependent on the text mining techniques that have been applied. The more a machine-learning algorithm performs a task, the better it becomes at it.
Text mining applications
The applications of text mining are quasi-infinite given the abundance of data in the world. Here are the most frequent text mining applications :
- Business analytics
- Conversation analysis
- Voice of customer
Data management and cloud storage solutions have had an overwhelmingly positive impact for businesses since the mid-00s, with companies big and small taking advantage of technological advances to grow their operations. Text mining techniques can be used to categorize and process all incoming data, from shipping details to customer behavior.
Conversation analysis is the study of verbal and non-verbal communication in everyday life situations. This field of study greatly benefits from access to large online databases, which act as the perfect ecosystem for analysis. Since text mining tools can also incorporate audio, image, and video into the process, this also means conversation analysis can make use of this kind of data when producing results.
Voice of Customer (VOC) Market Research
Voice of Customer is a marketing term that refers to a summary of customer preferences and expectations. Normally, VOC documents follow a hierarchical structure, where the varying needs of the customers can be contrasted based on their strategic value.
In the past, acquiring such information could only be achieved via customer surveys, focus groups, interviews, and other traditional market analysis methods. With the availability of social media platforms, modern customers provide their opinions online unprompted. Performing a sentiment analysis or similar operations guarantee those opinions won’t fall on deaf ears.