In the recent era of digital technologies, Text is the most popular means of communication between a given set of individuals. Whether it is a social media post, an email, or a text message, it has always been easier to express our thoughts in the form of texts. By accumulating billions and billions of data, there arose a possibility to harness that text-based information and analyze it for different business benefits.
DeepText, a Deep Learning-based engine developed by Facebook, is an example of such an idea turned into a real-life business model. Facebook uses DeepText to leverage the innumerable amount of textual data posted by its users and, in turn, analyze that information to display relevant content. For example, a user showing greater interest in movies by checking in at different movie theatres would be frequently presented with movie recommendations or movie clips whenever he logs into Facebook. In simple terms, the content you see on your Facebook newsfeed results from an in-depth analysis of your very own data.
Natural Language Processing (NLP) combined with Deep Learning can categorize a user’s interest based on their language. For instance, when a foreign user updates a post in their native language, Facebook presents it with a “Translate” option for non-native speakers. Another great example of NLP in action is the voice assistants such as Alexa and Google Home. They can create a voice profile of the owner and tell the difference between them and any newcomers. NLP helps these devices identify the language, slang, and sentence formation of each individual to differentiate between users.
How does Text Mining benefit a Business?
It has been estimated that more than 80% of business-related information originates from unstructured data, which is more commonly through text. Analyzing the data and converting it into a structured piece of information could widely benefit a particular business in various aspects.
Progressive Business Decisions
Data analytics that can provide accurate business insights is an essential aspect of a company’s development. In the recent era of perpetual chatter on the web, text mining plays a crucial role in effectively utilizing the produced data. The main reason is its operational efficiency. Text Mining software can analyze millions of unstructured data and understand a given set of patterns to provide/recommend more accurate results to the users. This is not only efficient but allows the decision-making capabilities of a user.
Consumer Engagement
Sentiment Analysis is one of the areas where Deep Learning and NLP are extensively used. Understanding how a particular consumer interacts with their product can help them build a more personalized experience for its users. For example, Amazon could analyze a user’s previous order data, the items in their cart, and their wishlist to recommend similar shopping items they might be interested in. This could promote a positive response among the customers and help to build trust in using the product.
Improved Risk Detection
With the accumulation of a large amount of data and paperless transactions, it becomes a tedious task to determine a potential risk factor associated with them. Major companies involving financial transactions such as banks and law firms use text mining to track every customer-submitted information to find out any irregularities or compliance issues. Monitoring and Analysis of online plain text sources such as Blogs, News is also done to spot potential threats or incorrect information that could cause a national disturbance. More recently, Facebook employed Text Mining combined with Deep Learning to identify fake news spread across the social platform.
Learn more about how Text Analysis and Natural Language Processing are carried out using ElasticSearch. Click Here to view the infographic.
