Tag: text mining

Natural Language Processing (NLP) Techniques & Examples

Posted on by Emily Louise Spencer

Data is key to understanding what customers want and need. But sifting through mountains of data and analyzing it can prove a daunting undertaking. That’s where advanced AI tools come in. In this article, we’ll discuss natural language processing techniques (NLP) and share examples of their application, examining how they can drive your growth.

The AI revolution is coming. Today, 35% of companies report using AI in their business, an increase of four percent from 2021. And an additional 42% report that they are exploring ways to begin using AI. 

No matter where you are in terms of readiness to begin adopting artificial intelligence and machine learning in your company, it’s to your organization’s benefit to learn about these emerging technologies and understand how you might be able to apply them in order to improve business outcomes. 

Natural language processing, or NLP for short, is the perfect place to start. 

It’s a powerful application of machine learning technology that can be used in a wide variety of industries for countless applications to help with everything from streamlining business processes to boosting efficiency to improving e-commerce customer experience and brand loyalty.

In this article, we’ll dive into everything you need to know about natural language processing including: 

  • What it is.
  • Its advantages.
  • Relevant techniques.
  • Applications.
  • And, finally, real-world examples.

Let’s start from the top.

What is natural language processing?

Natural language processing is a branch of artificial intelligence that aims to help computers to understand human language input in the form of text or speech. 

NLP combines multiple disciplines, including computation linguistics, machine learning, deep learning, and statistics. 

These technologies work together to essentially give computer software the ability to process and understand human language in the way that another human could, including its meaning, intent, and sentiment. 

NLP technology is used in a variety of applications including:

  • Digital assistants such as Siri.
  • Speech-to-text dictation software.
  • Voice-operated GPS systems.
  • Customer service chatbots.
  • Predictive text.
  • Digital voicemail.
  • Autocorrect.
  • Search autocomplete.
  • Email filters.

Additionally, companies are increasingly using NLP to create enterprise solutions that help businesses simplify processes, increase productivity, and streamline operations.

The benefits of employing natural language processing

It’s standard these days for companies to collect, store, process, and analyze large quantities of numerical data in order to generate valuable insights that can improve results. 

Natural language processing opens up and empowers businesses to make smarter decisions that are based on larger sets of data. Further, this collection and analysis process happens quickly, especially compared to traditional methods.

For this reason, natural language processing has a number of relevant advantages. 

When working with so much data, you’ll be able to generate insights to improve customer experience with the launch of new products.

On top of that, using NLP helps businesses become more efficient by automating work processes that require reviewing or analyzing texts. This frees up employees to work on other needle-moving tasks.

Taken together, you’re bound to see improved productivity, reduced costs, and an uplift in revenue.

The top techniques used in NLP

NLP is a rich field requiring the use of a number of different techniques in order to successfully process and understand human language. Below, we review and define a selection of the techniques commonly used in NLP technology. 

Tokenization 

Also called word segmentation, tokenization is one of the simplest and most important techniques involved in NLP. 

It’s a crucial preprocessing step in which a long string of text is broken down into smaller units called tokens. Tokens include words, characters, and subwords. They are the building blocks of natural language processing, and most NLP models process raw text on the token level.

An example from Medium of how a simple phrase can be broken down into tokens.

Stemming & lemmatization

After tokenization, the next preprocessing step is either stemming or lemmatization. These techniques generate the root word from the different existing variations of a word. 

For example, the root word “stick” can be written in many different variations, like:

  • Stick
  • Stuck
  • Sticker
  • Sticking 
  • Sticks
  • Unstick

Stemming and lemmatization are two different ways to try to identify a root word. Stemming works by removing the end of a word. This NLP  technique may or may not work depending on the word. For example, it would work on “sticks,” but not “unstick” or “stuck.” 

Lemmatization is a more sophisticated technique that uses morphological analysis to find the base form of a word, also called a lemma. 

The difference between how stemming and lemmatization work is illustrated in this image from itnext, using different forms of the word “change.”

Morphological segmentation

Morphological segmentation is the process of splitting words into the morphemes that make them up. A morpheme is the smallest unit of language that carries meaning. Some words such as “table” and “lamp” only contain one morpheme. 

But other words can contain multiple morphemes. For example, the word “sunrise” contains two morphemes: sun and rise. Like stemming and lemmatization, morphological segmentation can help preprocess input text. 

John Hopkins shows morphological segmentation by breaking the word “unachievability” into its morphemes.

Stop words removal

Stop words removal is another preprocessing step of NLP that removes filler words to allow the AI to focus on words that hold meaning. This includes conjunctions such as “and” and “because,” as well as prepositions such as “under” and “in.” 

By removing these unhelpful words, NLP systems are left with less data to process, allowing them to work more efficiently. It isn’t a necessary step of every NLP use case, but it can help with things such as text classification. 

Examples from geeksforgeeks of what short phrases look like with the stop words removed.

Text classification

Text classification is an umbrella term for any technique used to organize large quantities of raw text data. Sentiment analysis, topic modeling, and keyword extraction are all different types of text classification. And we’ll talk about them shortly.

Text classification essentially takes unstructured text data and structures it, preparing it for further analysis. It can be used on nearly every text type and help with a number of different organization and categorization applications. 

In this way, text classification is an essential part of natural language processing, used to help with everything from detecting spam to monitoring brand sentiment. 

Some possible applications of text classification include:

  • Grouping product reviews into categories based on sentiment.
  • Flagging customer emails as more or less urgent.
  • Organizing content by topic.

Sentiment analysis

Sentiment analysis, also known as emotion AI or opinion mining, is the process of analyzing text to determine whether it is generally positive, negative, or neutral. 

As one of the most important NLP techniques for text classification, sentiment analysis is commonly used for applications such as analyzing user-generated content. It can be used on a variety of text types, including reviews, comments, tweets, and articles. 

The Revuze platform employs sentiment analysis to understand how customers feel about various aspects of products. This allows companies to gain insights about consumers’ needs in real-time, and act accordingly to improve overall CX.

In this example from the Revuze platform, you can see how customers rate different aspects of the product.

Topic modeling

Topic modeling is a technique that scans documents to find themes and patterns within them, clustering related expressions and word groupings as a way to tag the set. 

It’s an unsupervised machine learning process, meaning that it doesn’t require the documents it is processing to have previously been categorized by humans. 

A sample NLP workflow from Frontiersin demonstrates how Input text is proprocessed before undergoing topic modeling, which breaks it into several topics. 

Keyword extraction

Keyword extraction is a technique that skims a document, ignoring the filler words and honing in on the important keywords. It is used to automatically extract the most frequently used and essential words and phrases from a document, helping to summarize it and identify what it’s about. 

This is highly useful for any situation in which you want to identify a topic of interest in a textual dataset, such as whether there is a problem that comes up again and again in customer emails. 

Text summarization

This NLP technique summarizes a text in a coherent way, and it’s great for extracting useful information from a source. While a human would have to read an entire document in order to write an accurate summary of it, which takes quite a bit of time, automatic text summarization can do it much more quickly.

There are two types of text summarization:

  • Extraction-based – This technique pulls key phrases and words from the document to make a summary without changing the original text.
  • Abstraction-based – This technique creates new phrases and sentences based on the original document, essentially paraphrasing it.

An example from the Microsoft tech community of how the two types of text summarization work.

Parsing

Parsing is the process of figuring out the grammatical structure of a sentence, determining which words belong together as phrases and which are the subject or object of a verb. This NLP technique offers additional context about a text in order to help with processing and analyzing it accurately. 

This is how parsing might work on a short sentence.

Named entity recognition

Named entity recognition (NER) is a type of information extraction that locates and tags “named entities” with predefined keywords such as names, locations, dates, events, and more. 

In addition to tagging a document with keywords, NER also keeps track of how many times a named entity is mentioned in a given dataset. NER is similar to keyword extraction, but the extracted keywords are put into predefined categories.

NER can be used to identify how often a certain term or topic is mentioned in a given data set. For example, it might be used to identify that a certain issue, tagged as a word like “slow” or “expensive,” comes up again and again in customer reviews. 

A sample by Shaip of how named entity recognition works. 

TF-IDF

TD-IDF, which stands for term frequency-inverse document frequency, is a statistical technique that determines the relevance of a word to one document in a collection of documents. It works by looking at two metrics: the number of times a word appears in a given document and the number of times the same word appears in a set of documents. 

If a word is common in every document, it won’t receive a high score, even if it appears many times. But if a word frequently repeats in one document while rarely appearing in the rest of the documents in a set, it will rank high, suggesting it is highly relevant to that one document in particular. 

Natural language processing applications 

NLP is a quickly developing technology with many different applications for organizations of every kind. Some of the different ways a business can benefit from NLP include:

  • Machine translation – Using NLP, computers can translate large amounts of text from a target to a source language, which can be used for customer support, data mining, and even publishing multilingual content.
  • Information retrieval – NLP can be used to quickly access and retrieve information based on a user’s query from text repositories such as file servers, databases, and the internet.
  • Sentiment analysis – This NLP technique can be used to monitor brand and product sentiment to help with customer service and product sentiment, among other applications.
  • Information extracting – This process, which includes retrieving information from unstructured data and extracting it into structured, editable formats, can be used for business intelligence, including competitive intelligence.
  • Question answering – Question answering uses NLP to give an answer to a question asked in natural human language and can be used for chatbots and customer support.

Natural language processing examples

Here are just a few more concrete examples of ways an organization might apply NLP to its business processes.

NLP in ChatGPT

One of the most popular recent applications of NLP technology is ChatGPT, the trending AI chatbot that’s probably all over your social media feeds. ChatGPT is fueled by NLP technology, using a multi-layer transformer network to generate human-like written responses to inquiries submitted in natural human language. ChatGPT uses unsupervised learning, which means it can generate responses without being told what the correct answer is. 

ChatGPT is an exciting step forward in the application of NLP technology for businesses and individuals alike, with many saying it can rival even Google. Possible uses for ChatGPT include customer service, translation, summarization, and even content writing. 

NLP for customer experience analytics

Using NLP for social listening and customer review analysis can lead to tremendous insight into what customers are thinking and saying about a brand and its products. With sentiment analysis and text classification, companies can:

  • Understand general sentiment about the brand – Does the public feel positively or negatively about us? 
  • Identify what customers like and dislike about a service or product.
  • Learn what new products customers might be interested in.
  • Know which products to scale and which to pull back on.
  • Discover insights that can be used to improve customer experience and boost customer satisfaction. 

For example, let’s say spicy chocolate brand Shock-O just released a new Popping Jalapeno Chocolate and wants to get a sense of whether or not customers like it. Shock-O can use an NLP-powered tool to analyze customer sentiment and learn what people are saying about the Popping Jalapeno Chocolate, whether they speak about it positively or negatively, and what themes come up again and again in reviews of this product. 

All of this information can then be used to determine whether to continue producing Popping Jalapeno Chocolate, whether to increase or decrease its production of it, whether to make it spicier or less spicy, etc. 

NLP for customer service

90% of customers believe that it is essential or very important to receive an immediate response when they have a question. Yet human customer service representatives are limited in availability and bandwidth. 

This is just one reason why NLP-powered chatbots are growing in popularity. By being able to properly understand and analyze customer inquiries, chatbots can offer the necessary answers to questions, helping to improve customer satisfaction while cutting down on agents’ workload.

NLP can also be used to process and analyze customer service surveys and tickets in order to better understand what issues customers are having, what they’re happy with, what they’re unhappy with and more. All of this serves as crucial data for boosting customer happiness, which will, in turn, increase customer retention and improve word-of-mouth.

NLP for recruitment

HR professionals spend countless hours reviewing resumes in order to identify suitable candidates. NLP can make this process much more efficient by taking over the screening process and analyzing resumes for certain keywords. 

For example, you might set up an NLP system to flag any resume that uses the word “Python” or “leadership” for a human to review later on.

This can increase the likelihood of finding strong candidates, helping an organization fill open positions more quickly and with better talent. What’s more, it can also free up HR professionals’ time to focus on tasks that require more strategic thinking.

Conclusion

The idea that data has important insights to offer companies has been widely accepted, leading businesses to invest in various business intelligence technologies in order to improve their processes and offerings. 

But if your organization is only mining numerical data, you’re missing out on a wealth of valuable information to be found in unstructured human language-based data. 

Natural language processing is a powerful technology allowing text and words to be analyzed as efficiently as numbers can. By learning about and investing in NLP, you’ll be able to achieve a number of desirable outcomes, including streamlining processes, improving brand reputation and loyalty, and ultimately boosting revenue.

The next step would be taking these actionable insights and using them to further drive CX with e-commerce personalization.

 

What Is the Difference Between Text Mining and Sentiment Analysis?

Posted on by Simone Somekh

A few years back, data was a vogue word; but things have dramatically changed. We are now in the era of big data; most businesses depend on data for their daily transactions and decision-making.

A Forbes article reports that the amount of data created, captured, and copied in 2020 reached 59 trillion gigabytes; an almost whopping 5,000% departure from the 1.2 gigabytes of 2010. While a large volume of data is created and downloaded daily, it’s important to note that the vast majority of the data we can find online is unstructured.

Data that can be used for business purposes and decision-making must be in a structured format, and this is where the problem lies, as most of the data out there is not structured. Technology is advancing at a very rapid speed and with tools such as text mining and sentiment analysis, the problem of structuring and analyzing large volumes of data is now automated.

Text mining, or text data mining, is the process of transforming unstructured text into a structured format; having 80% of data in the world residing in an unstructured format, text mining enables you to identify meaningful patterns and new insights.

On the other hand, sentiment analysis — or opinion mining  — leverages natural language processing (NLP) to classify data or reviews into positive, negative, or neutral sentiments.

While the two processes might appear to be similar, there is a world of difference between them. But first, it’s necessary to understand what data formatting is before exploring the differences between text mining and sentiment analysis.

  • Structured data: This is the format that can easily be used by organizations since the standardization into a tabular format with numerous rows and columns that can include names, addresses, and phone numbers allows you to store and analyze with machine learning algorithms.
  • Unstructured data: This format is not predefined. You can source unstructured data from social media, product reviews, video and audio files, as well as Q&A forums.
  • Semi-structured data: The name depicts that it’s a mix of structured and unstructured data formats. To an extent, it has a level of organization, but it lacks the requirements of a relational database; you still need to do some sorting to qualify it for analysis. XML, JSON, and HTML files come under this format.

The essence of exploiting text mining and sentiment analysis is to make better business decisions. Advanced analytical techniques, such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, are enabling organizations to discover hidden relationships and make better sense of their unstructured data.

Text mining vs. text analytics

It’s not uncommon to have people mix up the terms text mining and text analytics. While text mining is extensively used to derive qualitative insights from unstructured text, text analytics is used to provide quantitative results. You can use text mining to understand if a customer is happy with your product through the analysis of reviews and surveys. 

To have a deeper insight such as identifying a pattern like a negative spike in customers’ experiences or trends, you use text analytics.

The relationship between web analytics, text mining, and sentiment analysis

Virtually every company or organization has a website today. Customers visit websites to source products and services; they leave large volumes of data on their trail through the visits and actions they perform online. Web analytics enables you to collect, report, and analyze website data.

However, you need to integrate text mining and sentiment analysis to make useful sense out of the data that you gather from your website. Data from most visitors are usually unstructured; text mining will be used to structure the data, while you deploy sentiment analysis to understand the real significance and nuances in the data.

With this, you can determine the success or failure of those goals, have a data-driven strategy and improve the user’s experience.

The differences between text mining and sentiment analysis

Let’s take a look at the main differences between text analytics and sentiment analysis:

Text mining Sentiment analysis
What it does: Shows what has been written by customers about your product or service; what ideas are commonly linked in the text. It also shows which subjects and topics are most discussed by users and customers. What it does: Allows you to understand if your customers are reviewing your products or service positively, negatively, or neutrally. You can even go beyond non-text feedback, such as video, audio, and images. When a customer smiles, you can easily understand that the customer is satisfied compared to when a customer frowns.
How it can help you: Helps identify early warnings as an indication that your organization is heading into troubled waters or that there is an issue with your product or service. How it can help you: Negative scores indicate that your customers are on the verge of churning your product or service.
How it works: A patented NLP technology processes text-based data just like the human brain, but this is done with proprietary algorithms to identify parts of speech, words, or ideas that are linked, and comprehensively determine patterns and trends in your database. How it works: The focus is on determining whether words and phrases are positive, negative, or neutral. This is mostly done on a scale of -1 to +1, where -1 is extremely negative and +1 is absolutely positive.  

Popular text mining techniques

A lot of activities go into text mining; these activities are essential for the deduction of useful information from unstructured data. You, however, must begin with text processing for the cleaning and transformation of data into a usable format.

Tokenization, part-of-speech tagging, language identification, chunking, and syntax parsing are necessary steps for proper data formatting before you can embark on the actual analysis. After the completion of text processing, you then proceed with text mining algorithms for veritable insights from your data. 

Some common techniques you can use for text mining techniques include:

  • Information Extraction (IE)
  • Natural Language Processing (NLP)
  • Data Mining (DM)
  • Information Retrieval (IR)

Information retrieval (IR)

Information retrieval is the automated process that responds to a set of predefined queries or phrases to enable the return of relevant information or documents. IR systems can accomplish this task by using algorithms to track user behaviors and discover any data that is relevant. 

Library catalog systems and search engines such as Google make use of information retrieval. 

Some tasks you can use IR to execute include:

  • Tokenization: Enables you to break down a text that is long-formed into sentences and words called “tokens.” The tokens become the input for other processes such as parsing and text mining. 
  • Stemming: This is the process of removing the suffixes and prefixes attached to words. The essence is to have only the word stem. It’s very important in NLP. When you do stemming, it improves IR by reducing the size of indexing files.

Natural language processing (NLP)

Natural language processing (NLP) is that branch of artificial intelligence (AI) that gives computers the ability to understand the text and spoken words the way humans do. By combining computational linguistics with statistical, machine learning, and deep learning models, NLP enables computers to use these technologies to process text or voice data with a clear understanding.

Some sub-tasks you can use NLP to do include: summarization, PoS tagging, text categorization, and sentiment analysis.

Information extraction

Information extraction (IE) is an automated process of extracting structured data such as entities, entities relationships, and attributes describing entities from unstructured data, and storing the information in a database. Some sub-tasks of IE include feature selection, feature extraction, and named-entity recognition (NER).

Data mining

When you have big data sets, and you are trying to identify patterns and extract useful insights, you can use data mining. This technique helps you evaluate structured, unstructured data, and semi-structured data to obtain new information. 

Sales and marketing professionals can deploy data mining for the analysis of consumer behaviors. 

Conclusion

The processes involved in gathering customers’ data, and analyzing their sentiments can be overwhelming, but it is absolutely necessary for any brand that wants to remain competitive and relevant in the global market. Text mining and sentiment analysis must go together for you to improve customer experience, and embarking on this manually will ordinarily take you months. 

Revuze has integrated AI into sentiment analysis, which is what you need to actually classify your customers’ sentiments into positive, negative, and neutral. A platform like Revuze can automatically carry out the gathering, collation, identification, and extraction processes of trending discussion topics from any set of unstructured data.

Nowadays, understanding context with exceptionally high precision and delivering actionable business insights is of high essence, and that’s where Revuze comes in.