What is text processing Methods?

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Introduction

Thanks to advances in artificial intelligence, computing can do some things that the human brain can do. One of these advances is text processing, which refers to natural language processing.

Data cleaning or preprocessing in any machine learning task is as crucial as model building. Text data is one of the most unstructured forms of data available, and when it comes to working with human language, it is too complex. Have you ever thought about how Alexa, Siri, and Google Assistant can understand, process, and respond to human speech. NLP is the technology behind it, where there is a lot of pre-processing of the text before any response. This blog is a deep dive into word processing and how it can create value for the business.

What is Text processing?

Unstructured text data can be automatically analyzed and sorted by text processing to obtain useful information. Text processing systems can automatically comprehend human language and derive value from text data using Natural Language Processing (NLP) and machine learning, a branch of artificial intelligence.

Because we naturally communicate with words, not numbers, companies receive much raw text data through email, chat conversations, social media, and other channels. This unstructured data contains insights and opinions on various topics, products, and services. Still, companies must first organize, sort, and measure the text data to access this valuable information.

Product teams can use word processing to gather insights from customer feedback to help build their product roadmap. In contrast, customer support teams can use it to automate processes like ticketing and routing.

Why is Text processing essential?

Since Text processing is one way of Machine Learning, average technology consumers don't even realize they're using it. Still, most people use applications daily that use word processing behind the scenes.

As our interactions with brands become increasingly online and text-based, text data is one of the most important ways for companies to gain business insights. Text data can show a business how its customers search, buy and interact with its brand, products, and competitors online. Machine learning text processing enables enterprises to process these large amounts of text data.

Text processing methods

Let's look at some of the most crucial approaches and techniques for evaluating and sorting textual data now that you are more familiar with text processing.

The basis of text processing is mathematics and statistics. You can use all these statistical methods to process and analyze text from a frequency distribution, collocation, concordance, and TF-IDF.

You might be wondering what all these statistical approaches entail. Well, let's give you a quick overview:

1. Word Frequency

This statistical method accurately determines the most frequently used words or expressions in a particular section of text. With this specific insight, you can address problematic situations, identify areas of success, and more.

2. Collocation

This method helps identify co-occurring words – meaning they commonly occur together. The most frequent kinds of collocations in text are bigrams (two adjacent words) and trigrams (three adjacent words). For example, keeping in touch or launching a product are standard connections.

3. Concordance

By examining how particular words are employed in various settings, concordances effectively help to decipher the ambiguity of human language. The term "problem," for instance, can refer to a number of situations, including an issue, a situation, a topic, or the process of supplying something:

•  There was a problem with my account → problem
•  We have to solve → situations
•  It is a crucial topic → topic
•  Your tracking number has been issued → delivered

4. TF-IDF

TF-IDF stands for Inverse Document Frequency. This metric measures how important a word is to a document but is offset by the number of documents that contain the word.

For the sake of simplicity, let's use the following example: "the" or "and" are commonly used in all publications, making them ineffective for identifying certain subjects or topics included in a collection of documents. Imagine, however, that only one document contains several instances of the word "RAM." The "uniqueness" of this word can allow one to grasp what is being discussed in a certain paper.

5. Text Summarization

Text summarization is the practice of applying natural language processing to reduce complex technical, scientific, or other jargon to its most straightforward components.

This may seem daunting – our languages are complex. But by using basic algorithms for concatenating nouns and verbs, text summarization software can quickly synthesize complicated language to produce concise output.

Try text summarization by adding your own text to the model below:

Modeling topic
Topic Modeling is an unsupervised natural language processing technique using Artificial Intelligence programs to label and group text groups with common themes.

You can think of this as a similar exercise to keyword tagging, extracting and tabulating essential words from a text, except applied to topic keywords and associated clusters of information.

6. Text classification

Again, text classification organizes large amounts of unstructured text (meaning the raw text data you receive from your customers). Text classification includes several subdivisions, including topic modeling, sentiment analysis, and keyword extraction (which we'll discuss next).
Text classification takes your text dataset and then structures it for further analysis. It is often used to extract valuable data from customer reviews and customer service logs.

7. Keyword extraction

The last key to the text analysis puzzle, keyword extraction, is a broader form of the techniques we've already discussed. The most pertinent information from text is automatically extracted using machine learning and artificial intelligence (AI) techniques.
You can shape your software to search for keywords relevant to your needs – try it out with our sample keyword extractor.

8. Lemmatization and stemming

Lemmatization and stemming, which is more complex than our other topics, is the segmentation, labeling, and reorganization of textual data according on a root stem or definition.

Even though it can appear like you're stating the same thing again, both sorting techniques can yield useful information that is different. In our Text Cleaning for NLP guide, find out how to maximize both approaches.

That's a lot to grasp at once, but if you comprehend each step and study the lessons that are linked, you should be well on your way to a seamless and effective NLP application.

Text processing use cases and applications

Text processing helps businesses automate processes and extract valuable insights from data. This ultimately leads to better decision-making processes. This section will focus on customer feedback and customer service, which can be improved with word processing tools.

Customer feedback

Customer feedback is critical to any business strategy because it lets your customers know that you value their opinion. And, of course, it doesn't hurt to get valuable information about your company, product, or service.

In general, customers use a variety of platforms to express their opinions about your business. Still, the best way to get valuable feedback is through open-ended responses in surveys and product reviews. How might word-processing programs assist you in maximizing this feedback?

1. Analyze client feedback

Customers are often asked to assess your company on a scale of 0 to 10, and this is one of the most common ways for businesses to gauge customer happiness. How likely are you, for instance, to tell a friend or work colleague about this brand? You can categorize your clients as promoters, passives, or detractors based on the answers to this question.

2. Analyze product reviews

Customers are guided toward or away from products by product reviews, much like a compass. Consider the introduction of the iPhone 11 Pro. The annual release of Apple's latest smartphone generates a flood of online discussions that are a great source of information. These discussions give Apple a deep level of understanding about which features are or aren't hits, how customers feel about pricing, opinions on aesthetics, and much more.

Customer service

Customer service is about strengthening relationships and building customer loyalty. Customer service teams typically deal with a lot of customer inquiries. With word processing, you can automate processes so support agents can save valuable time that could be better spent actually helping customers.

1. Automatically flag support tickets.

When customers send a request, ask about a product or service, or complain about a problem or error, this information needs to be processed and processed. A large part of taking care of support tickets involves processing each to ensure that the appropriate team takes ownership and resolves the issue quickly and accurately.

But let's face it: categorizing tickets is tedious and time-consuming. By combining text processing with machine learning, you can automatically identify the topic of each support ticket and label it accordingly.

2. Route and sorting support tickets

When support tickets are highlighted, you can instantly channel problems to the appropriate party, speeding up teamwork and minimizing response times. Let's assume you receive a ticket titled Login Issues. Classifiers can assist your business automatically route issues depending on topic, language, urgency, and more. The IT department will get that ticket.

3. Determine the urgency of the ticket

The ability to prioritize tickets based on urgency positively impacts your business. For example, you can use a sentiment analysis model to uncover dissatisfied customers or an urgency detector to find issues requiring immediate action.

Conclusion

These are but a few examples of natural language processing strategies. Once information is extracted from unstructured text using these methods, it can be directly consumed or used in clustering exercises and machine learning models to increase their accuracy and performance.

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