Text analysis, as a whole, is an emerging field of study. Fields such as Marketing, Product Management, Academia, and Governance are already leveraging the process of analyzing and extracting information from textual data. We discussed the technology behind Text Classification, one of the essential parts of Text Analysis. Text classification or Text Categorization is the activity of labeling natural language texts with relevant categories from a predefined set. In laymen terms, text classification is a process of extracting generic tags from unstructured text. These generic tags come from a set of pre-defined categories. Classifying your content and products into categories help users to easily search and navigate within website or application.
The knowledge products that can be generated using text analysis are capable of being deployed to anybody’s desk.
– Steve Gardner, CEO, RowAnalytics
In this post, let’s talk about the current and emerging applications of text classification. We are using text classification to simplify things for us for a long time now. Classification of books in libraries and segmentation of articles in news are essentially examples of text classification. Adding the talk-of-the-day AI tech to it, the process just becomes automated and simpler with minimum manual work. The concept of using AI to classify text has been around for a fair amount of time (Automated filtering and Labels in Gmail, ring a bell?).
It can essentially be used whenever there are certain tags to map to a large amount of textual data. Especially in marketing, as it has moved from Search Engines to Social Media Platforms where real communication between brands and users take place. As marketing is becoming more targeted, marketers are using personalization to drive better engagements. Thus, listening to user conversations and analyzing them becomes a must-do task for marketers.
The classification can be done on any set of data. The ability of text classification to work on a tagged dataset (in the case of a CRM automation) or without it (Reading social sentiments online) just widens up the space where this technology can be implemented.
Applications and use cases:
1. Tagging content or products using categories as a way to improve browsing or to identify related content on your website. Platforms such as E-commerce, news agencies, content curators, blogs, directories, and likes can use automated technologies to classify and tag content and products.
2. Text classification can also be used to automate CRM tasks. The text classifier is highly customizable and can be trained accordingly. The CRM tasks can directly be assigned and analyzed based on importance and relevance. It reduces manual work and thus is high time efficient.
3. Text Classification of content on the website using tags helps Google crawl your website easily which ultimately helps in SEO. Additionally, automating the content tags on website and app can make user experience better and helps to standardize them. Another use case for the marketers would be to research and analyze tags and keywords used by competitors. Text classification can be used to automate and speed up this process.
5. As marketing is becoming more targeted everyday, automated classification of users into cohorts can make marketer’s life simple. Marketers can monitor and classify users based on how they talk about a product or brand online. The classifier can be trained to identify promoters or detractors. Thus, making brands to serve the cohorts better. 
6. Academia, law practitioners, social researchers, government, and non-profit organisation can also make use of text classification technology. As these organisations deal with a lot of unstructured text, handling the data would be much easier if it is standardized by categories/tags.
Text classification brings automation and simplification to the table. It is astonishing to see how the likes of Marketers, Product Managers, Designers, Academicians, and Engineers can all make use of this technology. The whole idea of technology is to make life simpler. Classifying large textual data helps in standardizing the platform, make search easier and relevant, and improves user experience by simplifying navigation.
Remarkably, machine intelligence and deep learning are planting roots at most unimaginable and orthodox areas as well. We may not have the concrete formula for flying cars hovering over the grounds as every 60’s kid predicted, but certainly, have one for taking them under the ground. The times are a-changing and they are exciting. Who knows what applications future might hold for Text Analysis.
