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Top 4 Natural Language Processing Applications for Businesses

Natural language processing applications

Natural Language Processing (NLP) has become more than an emerging technology. It has multiple applications in businesses today and is being widely used in various industries. NLP tools understand the meaning and context of phrases and provides automated support. They can be incorporated in any feature involving language, including online search, extract meaning, generate text, summarize a long document, or detect spam email.

Businesses need to apply different strategies to provide services to consumers as well as stay on top of the market. NLP aids this by improving customer experiences and streamlining processes. Here, we understand how Natural Language Processing applications can be applied to businesses.

Table of Contents

1. NLP for Sentiment Analysis

Natural Language processing applications can be great for companies to understand and analyze messages on social media. It helps them understand people’s opinions or feelings about their product or services. Sentiment Analysis assesses the attitude of a message by assigning emotions to texts, which can be positive, negative, or neutral. It also identifies the mood of the message, i.e. whether it’s happy, annoyed, angry, etc.

Sentiment Analysis lets organizations gain insight about their consumer's thought process and do a competitive analysis. It helps companies understand what customers feel about their brand and make necessary changes in business strategies. This evaluation helps to improve brand perception and customer experience.

Through text classification, NLP helps to identify words and phrases that are commonly used. For example, if the words ‘modern’ and ‘expensive’ are used in statements, it can be said that customers see you as a high-end, luxury brand.

Sentiment analysis helps get an overview of customer opinions, making this a time and cost-effective way of analyzing customer behavior.

2. NLP and Chatbots

Chatbots are the virtual assistants provided by companies to deal with customer complaints. They provide standard solutions and personalized assistance to common problems, and help off-load low-priority tasks that do not require a lot of skill. This is an efficient solution that helps save time, cost, and human effort.

In recent times, organizations have been applying Chatbots to optimize their work. HR is one of the areas that is optimized the most. For example, there is a Natural Language Processing tool called Talla, which answers employee questions like ‘How many leaves do I have left?’, or ‘When does my insurance plan take effect?’ The tool is called ServiceAssistant and works as an HR or IT help desk inside of Slack. Chatbot Polly, on Slack and Microsoft Teams, takes polls on workplace satisfaction.

Growbot, a Slack and Teams bot, monitors chats to analyze how often employees encourage/compliment each other. Employees are awarded for using encouraging words towards other employees such as ‘kudos’ and ‘well-done’.  This enables improving employee retention and morale in the workplace.

Chatbots can manage customer conversations, an example of this is Mastercard, which has enabled their banking partners to serve consumers over Facebook Messenger. Due to this, customer queries are solved without talking to a live representative.

Chatbots are important as customers want to be able to contact companies 24/7 and need immediate responses to their queries. Since it is difficult for companies to provide live chat agents day & night, chatbots can help on their behalf.


3. NLP for Customer Services

Keeping customers happy is the fundamental responsibility of every organization. NLP aids customer service by gaining insights into their tastes and preferences. Call recordings of customer interactions can provide information on whether they are happy about the services or not. This also makes it possible for the company to understand their future requirements as customer feedback is probably one of the most valuable data for businesses.

Interactions between companies and customers can have a lot of useful information that can point towards customer dissatisfaction. It can be a great foundation for marketing and advertising campaigns.

Companies don’t have to spend hours going through this type of qualitative data manually. NLP interprets and analyzes words, sentences, and context of your customer support queries. Speech can be translated into text messages through NLP which could be analyzed for more information.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

4. NLP Application in Market Intelligence

NLP can improve market intelligence in several ways through text analysis. Market intelligence consists of market knowledge or information shared between companies, governments, and regulatory bodies. Information through market intelligence can help companies build new strategies, and NLP can help in extracting this information from raw business data.

NLP can track and monitor market intelligence reports. This data may be in the form of sales and marketing data, product data, and brand reputation. NLP text analysis extracts important insights from unstructured data helping companies to make useful decisions and future strategies. It helps reveal patterns in scattered data which can be used for further analysis.

This is extensively used in Financial Marketing, as NLP provides detailed information such as status of the market, tender delays, and analyzes past earnings/annual reports to estimate future growth.

There are great benefits of NLP in Market Intelligence. Increasing data access and improved data quality will enable businesses to save costs, and enhance their decision-making capabilities.

Over the last decade, there has been a huge headway in different aspects of Natural Language Processing Applications. Organizations have been able to execute the most beneficial applications of NLP for the advancement of Business Intelligence.’s powerful NLP platform helps you get information from collections of unstructured texts through sentiment analysis, entity analysis, entity extraction, and content classification. Here's an example how text classification helps reduce the average call handling time by customer service representatives through's Contact Center Topic Modeling.