- Natural Language Processing Applications and Use Cases
- 1 Text Mining, Document Classification - Research and Analysis / Investigation
- 2 Data Analysis - Market Research / Business Intelligence
- 3 Sentiment Analysis - Brand Monitoring, Reputation Management, Customer Support
- 4 Conversational User Interfaces
- 5 Text Generation: NLP application examples
- 6 Text Summarization - News generation, Report Generation
- 7 Machine Translation NLP use cases
- 8 Semantic search - document search & management / research
- 9 Text Classification - Content Moderation / Spam Filtering
- 10 Text Classification, Sentiment Analysis - Service Personalization / Recommender engines
Natural Language Processing shapes our everyday lives without calling attention to itself.
- Spam filters - check
- Optical Character recognition - check
- Voice recognition - check
- Grammar check
You get the point.
NLP applications are present in the majority of data processing operations, especially in those that need analysis and generation of content. In the previous article, we explained what NLP is and how it works. In this article, we will take a closer look at the major business applications of this technology.
A text can be considered an entity of boundless possibilities. This is especially true if you have an idea of what you want to get out of it.
This is what text mining is all about.
The very purpose of text mining is to explore and extract specific insights hidden behind the walls of text. One of the uses of natural language processing is a wide range of research and investigation purposes.
The insights can be different:
- Valuable information in the text through semantic search (such as quotations, key facts, et al.)
- Specific details about a certain subject (figures, states, etc)
- Mentions of specific things in the texts (name mentions and so on)
- Structural analysis
The process looks like this:
- Information Extraction to get a hold of the unstructured text
- Categorization - to classify the contents of the text and the role of its elements.
- Clustering - to group pieces of content with similar or common elements
- Visualization to streamline the presentation and perception of the results
- Summarization - to form a concise presentation of what the text is about and what its features are.
In one way or another, text mining always turns up. The results of the text mining operation can be utilized in a variety of ways. We will look closer at some of them later in this article.
For now, let’s look at those that serve a specific business purpose:
- Topic Modeling - can be used to understand what the text and its elements are about. Can be used to extract viable figures and expressions.
- The same approach, combined with named-entity recognition, can be applied for tag optimization. The algorithm rounds up the most important words, such as names of people, organizations, and so on.
- Intent Analysis - can be used to analyze specific phrases and determine the intentions behind them. For example, it can be used to determine whether a customer is going to buy something, or if he still considering different options.
One of the most prominent tools for text mining and analysis is Voyant Tools. Built for scientific research purposes, it contains a wide variety of tools that help you extract all sorts of insights out of a document.
Data Analysis is an essential one of the applications of natural language processing. Like text mining, it can be used to dig deep into the roots of a specific document. You can use the same tools on a broader scale.
The text mining process can be used for more practical purposes by business intelligence analysts. Such purposes might include gaining business intelligence or performing market research. The thing is - texts, infographics, and images of pieces of news, are essential for further decision making.
The purpose of the scraper is:
- to check the sources (or look through with specific keywords)
- note the stuff of interest (determined by your business needs).
In essence, this process is data mining but oriented towards text-based data and related paraphernalia. Such applications can visualize and present insights within a couple of clicks with accompanying reports.
NLP helps to get a hold of this information without a fuss. Classification and categorization algorithms impact decisions.
As such, NLP can be used for the following purposes:
- To explore the market situation and coverage of specific subjects;
- To study the impact of your actions on the market.
- To study the competitor’s behavior.
- To explore the customer’s persona, needs, and demands.
- To find valuable information hidden in reports or other pieces of content.
- To be aware of the news about competition events, technologies, and other important events.
The critical point is that NLP makes Business Intelligence more accessible and diverse.
For instance, a couple of algorithms can save many hours of manual work and make it easy for the non-tech specialist to handle data on their own.
As a result, you get a lot of information gathered with less effort and more time to go deep into insights.
Sentiment analysis is the other prominent use of NLP for business operation. It can be considered as the secret weapon of companies. SA helps to navigate the dangerous seas of the market and avoid sharp edges.
Opinions and sentiments form an environment surrounding the product, plus its positive or negative impact on how the product is perceived and engages with the target audience. This matters when you are dealing with the product, service, or perception of the brand.
Sentiment analysis can be used for the following operations:
- market research, marketing analysis - about the competition, market news, emerging technologies
- public relations, reputation management - brand mentions, et al.
- expanded product analytics used in general and specific cases.
- Sentiment analysis can augment with the study of product reviews and customer support feedback.
All this and more can be extracted with NLP algorithms and subsequently be presented in a bigger context. Sentiment analysis can affect the decision-making process and help to react and adjust to the state of things.
If you want to learn more about the applications of sentiment analysis - we’ve got an article about it.
Speech recognition and speech synthesis are the most promising natural language processing niches.
Instead of relying on strict commands, machines are learning to interact with people on people’s terms.
These days, conversational interfaces are used in different industries with a similar purpose:
- To streamline the interaction with the customer
- To deliver more value to the interaction.
- To extract more information from the interaction.
- As a result, improve the quality of customer service.
Let’s look at the most prominent use cases:
- Customer support - the most basic use of conversational UI is also the most multi-faceted. Conversational customer support tells more about product use, emerging issues, and general sentiment. The natural language processing example is one of our projects, a NLP-fueled conversational UI can improve customer support in healthcare.
- The same approach can be used in the sales process. The CN streamlines the sale funnel and presents viable options based on user history and expressed preferences.
- Recruiting is another field where conversational UI can save a lot of time, and at the same time, present a lot of valuable information. In this case, NLP is used for the initial scraping of CVs according to set criteria. During the interview, the CI determines whether the candidate is compliant with the position or not.
- Onboarding. Additional natural language processing project ideas is conversational UI that can be used to train and get new employees on board in a more streamlined and casual manner. Instead of time-consuming explanations, you can lay out the basics in one swift conversation sequence.
- Task Management. Being productive is challenging with all the distractions and stresses surrounding us. It always tends to get messy over time. Conversational UI can help streamline routine operations and remind of pending tasks at the right time.
- Lead generation - the way people apply conversational interfaces in this field is similar to recruiting. The difference is in the peculiarity of the dialogue. It involves more intricate questioning and more strict delivery of facts in response to queries.
- Other natural language processing examples are CRM platforms like Hubspot and Salesforce are offering essential solutions. But, if you want to use conversational UI the way you see fit - it is better to go with a custom-built option.
If you want to read more on Conversational UI, we’ve got an entire article about it.
There are two entwined divisions of natural language generation:
- text generation
- text summarization
While their use cases often overlap, it is better to view them separately.
The difference is that text generation creates the original content but independent in its form. At the same time, summarization distills the information around the most valuable points.
Text generation often gets a bad rap due to its seeming “lack of creativity.” For example, a recent AI-generated manual was thrashed because it was generic, despite actually serving its purpose.
The root of the problem is in the way the technology is applied. The text can be generated for a specific goal based on particular source material.
Text generation creates highly structured documents that make the most out of available data. Then, the text generator presents the text in an understandable form.
In business, text generation is in a weird position, all things considered. While Natural Language Generation is used across different industries, it is rarely applied merely to a piece of text.
Often, the generated text is a result of the distillation of other content, which includes a summarization (more on that later) and not exactly the creation of the distinct piece.
There are many ways text generation can be useful in different aspects of business operation.
Report generation is the most exciting way for text generation in business operation. This process involves reiteration of the classified information into a more narrative form.
The thing is - information tends to get lost when handled manually, some of it gets more of the spotlight, while the rest is ignored. Text generator can handle this by only doing its job with the available data and zero bias.
For example, you can construct a performance analytics report with recurrent neural networks that include:
- the relevant figures on different aspects of the operation,
- comparison with the previous results,
- assumptions of the cause of the results
- predictions of future dynamics.
Research results are another field where text generation can be beneficial.
The thing is - research papers have the benefit of having a strict structure and excellent accessibility via topical terms. They are easy to deal with.
With the help of the database scraper - it is possible to compile a digest of results on a specific topic. This can be used to construct chronology and provide a different perspective on the problem.
In the same manner, the algorithm can present a variety of suggestions about solutions to a particular problem.
Text Summarization is probably the most intriguing use of Natural Language Processing today. In essence, it is the same thing used for text generation. Text Summarization processes the text and delivers its distillation.
There are several fields where text summarization is prominently featured:
- News Media
- Internal documentation
Let’s look at them.
1. Making news is hard enough, even if you don’t think about tight deadlines and thorough fact-checking. A news piece must meet specific editorial criteria, such as accuracy, timeliness, availability of sources, etc.
But, before all that starts to matter - the piece should be written.
On its own - writing a news piece is not a big deal. But, there is a lot of stuff going on in the world. And, the reality is that a news media platform must deliver news in time to remain competitive and engage with the target audience.
Thus, Text Summarization is a cost-effective and time-saving option for the media and journalists. Think about it as a helping hand for the journalist.
A fine-tuned algorithm reiterates the source text. Then, it generates a serviceable summarization in the form of a standard news piece.
You can use this text as raw material for further writing. Or, it can be used as a newsfeed filler so that the journalist can concentrate on research and analysis of the situation. Besides that, summarization can be used to fill social media and newsletters with reliable content.
2. In the context of analytics, text summarization takes the role of a verbal data visualization tool. It is a natural evolution of reporting that streamlines the routine part and gets straight to the point.
Textual summaries of stats can be convenient, especially when you need to present the results without missing any details.
Such reports interpret incoming data in a verbal form and provide a less strict and more flowing interpretation of data.
3. In the case of Search Engine Optimization, text summarization is a tool for exploration and studies.
Summarization can be used for content development (based on topic modeling and specific keywords).
It can also be used to analyze the content of competitors.
Summarization can create linking pillar pages, and improve the user's journey on the website.
4. The thing with internal documentation is that there is a lot of it, and it's tough to navigate through the hoards of data without a helping hand.
- Summarization may come in handy when one needs to understand what happened during a specific period according to the reports.
- Or, it can be used to study the progress of the project in a concise form.
- But, summarization is a friendly tool to present different points of view on a specific subject.
The process goes like this:
- The algorithm is plugged into the database or analytics platform.
- Upon selecting the requirements - it can reiterate the results by specific criteria in the same way the data is visualized. But instead of a bar chart, you get a sentence like “Traffic volume, according to sources, is “so-and-so.”
Computational linguistics and natural language processing have arisen because, during the Cold War, the United States wanted to decode messages without native speakers. It was part of scientific interest, and part practical necessity.
The results were mixed, and it turned out that it takes more than just translating the words to explain the meaning of the message into another language. It took quite a while before machine translation became capable of even remotely beneficial results.
Years of research and constant trial and error made natural language processing algorithms sophisticated enough to deliver the message across languages. Now you can easily present your company’s landing pages in several target languages without bending over backward. You can use it for content marketing and social media presence.
The mechanism behind machine translation involves the following:
- The system consists of two components in the respective languages.
- The components contain a general base of text with a wide variety of samples to map out the possible combinations.
- They are supplemented with an additional vocabulary (general purpose and subject-specific)
- The input text gets exploratory text analysis.
- Next is a comparative analysis of respective languages.
- The text is mapped out by elements.
- The elements are transported into the other language.
What’s the business benefit of translation? There are plenty.
- Multi-language content widens the audience reach beyond the primary language. For example, in the United States, you can go for the Spanish version as a second language.
- But, the multi-language presentation makes you inclusive to the untapped audience segments.
Getting through internal documents can be a dubious task. Since there are many little details and each of them is required to understand the state of things.
Hopefully, natural language processing can make it less tedious.
Semantic text analysis can be applied to the database of documents to map out their features. Then, the semantic search feature can be used to navigate within this database with ease.
This approach can streamline the workflow that requires constant referencing.
But, semantic search can be used for broader research.
The thing is - being able to find the information you need is one of the primary tasks in almost any field of activity. It also happens to be one of the most challenging tasks, due to the amount of routine and meandering in-between quality time. It is hard to surf through hordes of data to find those specs of gold. This is how we get the most value from the task.
Hopefully, an NLP solution can make it much easier by the implementation of semantic analysis and search features to the process.
If used for external research purposes, it requires an additional component of a scraper. Thus it will go through the sources by specific criteria. As a result, the system is capable of finding fitting information for the request.
In one way or another, content is always at the center of things. Content moderation is a critical practice that goes beyond simple platform maintenance. The quality of its work directly affects the perception of the platform.
If done right - it is all hunky-dory.
But if not - the platform can be perceived as toxic and make users want to change to something different.
Community guideline compliance is one of the cornerstones of social networking. It is a form of imposing an informal agreement between the service and users.
The thing is - keeping a balance in check is hard in cases of user-generated content or comments. It is simply too much.
Just think about how the average rate of posting on Facebook per minute is:
- 510,000 comments
- 293,000 statuses
- 136,000 photos
That’s a lot of content. And you need to be sure that all this stuff is compliant with community guidelines and bears no threat to the well-being of other users. Otherwise, troubles may appear.
Hopefully, NLP can handle it. You can use NLP to track what is going on your platform or its specific elements.
That’s where text classification comes in handy. Classification algorithms can identify a wide variety of elements in the content and take action in those cases when it is required.
Among other things, such algorithms can be used to:
- filter out hate speech occurrences, or extremist content
- block fake news drops.
- Identify cyberbullying
- Block outright spam messages and bot accounts
This framework is behind Facebook, Twitter, Instagram, LinkedIn, and other social networking platforms.
Also, in moderation, this approach gives you another channel of audience research. The incoming data can show different trends within the user base and help to identify those points of the most significant tension. Incoming information can help to improve the platform and keep users from going away from it.
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Keeping users engaged requires a significant effort. Service personalization is one of the most effective methods of user engagement. You get to know what users like more and present them with more stuff that is relevant to them to maximize the use of the service. In other words - if it gives useful stuff, why not use it more to get more helpful stuff.
In the same way, recommender engines are organized.
With NLP algorithms, you can build a monitoring system that will adjust the service to the needs and preferences of the particular user.
Here’s how it works.
- There is a user profile that gathers info on user behavior.
- It monitors the interaction with the content - for example, likes, dislikes, shares, comments.
- It shapes the matrix of preferences - what the user likes, dislikes, or neutral.
- From the other side, the system assesses the content user interacts.
- The algorithms round up the relevant topics and sources. NLP algorithm involves topic extraction and primary keyword navigation.
- As a result, the user is presented with more content that is relevant specifically for him.
Let’s look at how it is done on significant services:
- Facebook shapes newsfeed by using direct user input, and also by interaction with the content. The stuff which users interact with more gets a more significant share than the stuff users ignore.
- Google’s Search Engine adjusts search results to user behavior tendencies, i.e. expressed preferences. For example, if you are looking for entry-level materials on machine learning - the search queries are inclined to show more stuff like that.
- Youtube’s algorithm uses many metrics that shape user experience on the grounds of expressed preferences. The algorithms cover topics, metadata, user view time, user interaction with the content, search queries, and view history. All this creates a personalized experience that is different from other users.
- Amazon’s recommender engines track user preferences and shape the product suggestions accordingly. If you are looking at the brands in the high-profile category - you don’t get to see low-profile brands in-between. So, the algorithm suggests related products that may be relevant to the purchase (for example, a mouse for the computer).
Natural Language Processing's usefulness depends on the understanding of its capabilities.
It can be advantageous if you know how to use it. NLP can be utterly useless if you apply it to something it is not supposed to do.
This article has given several examples of how to use NLP for maximum effect, and how to get the most out of data for your company's benefit.
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