Text Mining: Definition
Text Mining can be defined as a technique for automating the processing of large volumes of text content . This technique, which combines the fields of linguistics, semantics, statistics and IT, is used to conduct marketing studies, satisfaction studies, monitoring, or in the context of social intelligence . Text Mining is used to identify and extract major subjects and trends in a set of texts, but also to determine the main areas addressed, or to analyze feelings and emotions . Text Mining is also used for monitoring.
Used in particular in marketing, Text Mining is based on a semantic analysis technique to process data available in digital format. Text Mining techniques are increasingly used to analyze a corpus of text content almost in real time, allowing, among other things, the detection of weak signals when managing a crisis.
On the internet, Text Mining can be used to analyze the content of incoming emails , in order to extract the main requests. Text Mining techniques are also particularly useful for analyzing customer reviews and comments made on social networks and other web pages. When conversations are transformed into text content using voice analysis techniques, Text Mining can also be applied to the field of call centers.
The current context of Big Data, propelled by the rise of the internet, is exploding the volumes of texts available for analysis, which contributes to ever more relevant results.
Text Mining at the service of marketing
Text Mining is particularly useful in the context of marketing studies, in order to allow a company or brand to better target the expectations of its customers . Indeed, faced with the multiplication of sources as well as the growth of customer reviews on social media, knowing how to decipher consumer messages is now crucial for businesses. An issue to which Text Mining can respond.
With regard to social networks, these have contributed to the liberation of the voice of consumers, who can easily share their customer experiences, give their opinions and express their needs. How can we best use this information which turns out to be gold mines for companies? Text Mining offers marketing sectors the possibility of knowing, almost in real time, consumers' opinions about their brand, products or services. Text Mining techniques also allow companies to position themselves vis-à-vis their competitors or within the framework of a particular market. Text Mining is therefore essential in Enterprise Feedback Management (EFM), a science allowing real-time analysis of customer feedback, in order to act to quickly satisfy them.
Many experiences in the field of EFM have demonstrated the power of Text Mining. For example, the recent use by a French bank of Text Mining techniques in order to understand how Internet users expressed themselves about their products and services. The results were particularly interesting for the marketing sector, since the study showed a poor understanding of the banking terms used by customers. The bank was thus able to adjust the terminology used in its advertising campaigns, on its websites and on social media. Account managers are also now trained to use the right semantics with their clients.
In general, the texts written by Internet users are particularly subjective and bear their opinions. These data reflect the real world that a company must successfully capture in its marketing campaigns. Textual data from large sites such as Amazon.com or Allociné.com, for example, is rather easy to analyze. On the other hand, content from sites with a large audience such as blogs or other sites dedicated to a specific universe sometimes prove to be more complicated to analyze. Text Mining is therefore of great help in exploiting this content., which, although they are more complex to analyze, prove to be very relevant because they are generally more detailed. As for textual data from social media, it is very valuable. Indeed, coupled with information from a CRM database , they allow companies to complete their customer knowledge, particularly in terms of interests and social position. The opportunities for such studies are then very vast for companies, if however they are able to extract exploitable information in order to transform them into relevant lessons.
What is effective text analysis?
Extracting and exploiting text data from social media , for example, are very time-consuming and complex tasks, sometimes daunting. The technology and the use of Text Mining techniques prove to be saving, however they do not bring all the answers by themselves. So, what are the sine qua non conditions for carrying out a textual analysis?
In a Text Mining project, the framing phase is particularly decisive. Above all, it is a matter of precisely identifying the sites and social media to analyze, based on the information that a company wishes to collect, in order to determine the right keywords to capture. The whole challenge of this phase being to retain a semantic field wide enough to capture the subject, while being precise enough to obtain relevant answers.
Companies must take their time to establish an effective framework, since this phase will lead to the choice of the right Text Mining tools. Indeed, Text Mining solutions are numerous on the market and do not bring the same results. Likewise, the possibilities offered by these tools are complex. It is therefore imperative that a company question its real needs before choosing Text Mining software: on which elements should textual analysis focus (emotion, content, etc.)? How should the results be shared? Etc.
Subsequently, the role of the analyst in charge of analyzing the data from Text Mining is essential. The latter must therefore intervene throughout the process, from the scoping phase of the project. He must be able to guide his company in choosing the right software and algorithms , while ensuring a correct analysis of the data collected in a given context.
The Text Mining outlets are obviously particularly vast and companies really need to be supported in this market, in order to opt for the right Text Mining technologies, appropriate to their needs. This is why they should not hesitate to be accompanied by professionals if they do not have sufficiently trained analysts in-house.