Natural language processing (NLP) is transforming how businesses operate by enabling machines to understand human language. NLP powers various applications like sentiment analysis, text classification, chatbots, and more, which are delivering immense value across industries. This integration showcases different types of research that are pivotal in developing these applications.

In this blog post, we will discuss the top 5 NLP applications that are driving innovation in the business world and why every company should pay attention to this technology today.

Why NLP is a Game-Changer for Businesses

Let’s first understand why NLP is becoming indispensable in the corporate arena. Here are some key reasons:

• Analyse Large Volumes of Text Instantly: NLP algorithms can process thousands of documents within seconds and generate actionable insights. This enables quicker and data-driven decision-making.
• Automate Time-Consuming Tasks: Several business processes involve analysing text data like support tickets, surveys, social media posts, etc. NLP bots can automate such tasks to boost efficiency.
• Improve Customer Experience: Applications like chatbots and voice assistants that leverage NLP are enabling personalised and instant customer support 24/7.

Top 5 NLP applications that companies need to know about

1. Sentiment Analysis

Sentiment analysis refers to automatically identifying and categorising opinions expressed in text to determine the writer’s attitude as positive, negative or neutral. For businesses, analysing sentiment can deliver powerful insights into customer opinions which drive decision making. 

Some key use cases include:

• Monitor brand mentions on social media
• Analyse customer feedback surveys
• Track reactions to marketing campaigns
• Identify detractors and negative experiences
• Guide product development cycles

2. Chatbots and Virtual Assistants

Chatbots and virtual assistants allow natural conversations between humans and machines to get answers to queries or resolve issues instantly. From simpler FAQ bots to complex voice-enabled assistants, NLP powers the ability of these tools to interpret requests accurately and respond with relevant information. 

Key benefits include:

• Instant 24/7 customer support
• Automate repetitive tasks for employees
• Reduce call volumes to human agents
• Consistent and accurate responses
• Scalable across communication channels

3. Text Classification

Text classification refers to organising and categorising text documents by applying labels or tags automatically based on their content. It is one of the most common NLP capabilities applied across business functions.

Use cases include:

• Customer Support: Classify support tickets by urgency, issue type, sentiment, etc. to route them faster. A ticketing system can detect priority tickets and escalate them.
• Marketing: Categorise prospect interactions to identify sales leads faster. Also, group social media conversations by topics that need responses.
• Business Intelligence: Structure open-ended survey responses to analyse key themes and spot trends. Sentiment analysis can provide deeper insights.

4. Text Extraction

Text extraction or information extraction focuses on identifying and extracting specific pieces of information from text automatically into structured data.

For instance, from a resume, the model can extract details like names, contact information, education degrees, companies and designation details accurately. Such features eliminate the need for manual data entry.

Some common examples include:

• Extract names, addresses, phone numbers, and email IDs from documents
• Extract product details like serial numbers, models, and specs from catalogues
• Extract keywords and keyphrases to understand text content
• Extract quantities, prices and other numeric information

5. Machine Translation

The exponential growth of globalisation has made cross-border communication integral for businesses. Machine translation uses NLP to translate text between languages accurately while retaining context and meaning.

The most popular example is Google Translate serving over 500 million people daily. Enterprise use cases are:

• Translate customer support content into local languages
• Break barriers and expand to new global markets
• Translate product catalogues, marketing collateral, manuals, etc.
• Gain insights from content published in foreign languages.
  

Key Takeaways

Natural language processing (NLP) is steering the wheel of automation and intelligence across various business functions today, making it a cornerstone in research design strategies. Virtually every enterprise stands to gain from integrating NLP capabilities such as text analytics, classification, extraction, and translation. These tools not only drive operational efficiency but also unlock avenues for growth by providing insights that can guide strategic decisions and innovative research designs.