Now that you’re familiar with what NLP is and its history, in this chapter we'll explore some real-world applications for NLP.
Sentiment analysis uses NLP to identify, extract, and analyze the tone used in textual data. The data is initially preprocessed to extract meaningful features before passing them to the algorithm. Sentiment analysis may use supervised or unsupervised machine learning techniques to classify the text as either positive, negative or neutral. A contextual analysis of words and phrases used can determine the emotions and affect of subjective texts, like customer reviews, opinions shared in chat forums, and so on.
For example, if you work with an e-commerce organization, you can use sentiment analysis to gain insights into product performance and reception. You can better understand how customers feel about a particular product by analyzing customer reviews using sentiment analysis, determining whether it’s a positive or negative review. Using NLP in this way helps organizations analyze product reviews—and other sentiments expressed in written form—at scale.
Spam detection uses algorithms to analyze and filter unwanted text based on set criteria automatically. NLP models typically use supervised learning algorithms to analyze the content of the messages and identify patterns and characteristics associated with spam. They are typically trained on labeled examples of spam and legitimate messages, allowing them to learn to distinguish between the two. These leverage text classification techniques that classify text as relevant or irrelevant—or appropriate and inappropriate—based on your defined terms.
Entity or data extraction automatically identifies and extracts specific types of entities or data points from unstructured textual data, such as names, addresses, phone numbers, dates, or prices. First, textual data is preprocessed, extracting features that are then passed on to NLP models that analyze them and identify specific entities. The data sources could be physical or scanned documents.
You can use NLP to perform data extraction tasks like scanning invoices, purchase orders, contracts, and other documents to collect different forms of data. The structured data collected can be used to feed other business systems, like enterprise resource planning (ERP) tools. Using NLP for data extraction reduces human error and saves you the time and trouble of collecting this data manually.
Text categorization and clustering involve tagging words in a phrase, organizing and grouping them based on their similarities or differences. Categorization is the process of assigning textual data to one or more predefined categories, based on their content or meaning. On the other hand, clustering involves grouping textual data into clusters or groups, based on their similarities or differences. NLP can organize vast amounts of textual data into topics, helping you better parse textual content and categorize the content for future analysis.
For example, you can use NLP to search for text that reveals qualities about your customers on social media or in product reviews—their interests, location, and so on—to organize customers into groups defined by that specific quality. This information allows you to create and promote products well-suited to your target customer.
Text summarization automatically produces a concise summary of a longer text like a book, blog article, or research paper. Text is then either entered verbatim or preprocessed to extract features before passing through NLP models that rank the sentences according to their importance and relevance. It then selects and combines the most important sentences to output the needed summaries.
You can use NLP to boost in-house education by summarizing long research papers in a specific domain, like machine learning, in a way that’s digestible and quick to read. Summarizing these texts saves time since the material is shorter, making retrieving key information and learning new material easier.
Articles for blogs, magazines, and newsletters help with content marketing. However, creating the content can be a demanding and cumbersome task. You can use NLP to generate content for your articles, like a list of article topics or the actual article copy. NLP models automatically generate meaningful textual content from a given input, such as a set of keywords, a prompt, or a structured data source. NLP techniques can generate articles by leveraging natural language understanding and machine learning algorithms.
A real-world example is using NLP to generate written content, like social media captions or blog posts. You can also use NLP to generate catchy and accurate titles, headlines, and descriptions for content as we do on our blog, Context by Cohere if you’d still like the article body to be written by a human.
A robust customer support approach is key to any successful business, but it can be challenging to scale. You can use NLP to create customer support chatbots to ensure customer inquiries aren’t missed or organize inquiries to ensure customer support teams are more productive.
These chatbots can be programmed to answer customers’ simple questions by identifying keywords and phrases and responding with predetermined, relevant information. This allows customer care agents to have more time to tackle complex queries.
A real-world example is creating a chatbot for an e-commerce website. Your bot can answer basic questions about product prices and delivery dates.
Financial analysis can be tedious, as it involves observing financial indicators and news trends to make informed decisions. But when you’re working in the finance industry, speed is everything.
Say you work on an app that shares daily updates on economic patterns, shifts in stock values, or other important financial moments. You can use NLP to scan financial news sites for important information quickly. After collecting that information, you can analyze, simplify, and share it with your users.
In this chapter, you learned some exciting applications of NLP and how it can impact day-to-day activities. As you progress with this course, you can develop your own applications that can leverage this technology. In the next chapter, you will learn about pre-processing textual data, a vital process in building NLP applications.
Updated about 1 month ago
Now that you know the applications, let's learn the ins and outs of NLP. The first step is to pre-process text.