What is Text Processing? [Definition & Methods]

There is no secret that data has become a new currency for businesses. The increasing use of technology has resulted in the generation of vast volumes of data.

Whether you use an app, send emails, fill in different forms, or leave a comment on social media posts, you create data containing valuable information. As many people do these, an enormous amount of data is generated every day, called big data.

Big data is a term used to describe large and complex data sets containing structured, semi-structured, and unstructured data. Companies leverage big data to make informed decisions, improve business strategies, and take any action that results in increased revenue and profits.

However, in its raw or original form, big data is useless as it is not easily digestible. It becomes tricky to derive knowledge from big data. This is where text processing comes into play. It is the process of automating the analysis and sorting of unstructured data or electronic text and extracting value from it.

Text processing with machine learning help companies and business extract meaningful information from big complex data.

In this article, you will learn everything about text processing.

What is Text Processing?

It is an automated process of analyzing and sorting text-based or electronic data into knowledge or valuable information. Machine learning models then use the structured data of the text for analysis, manipulating text, or generating new text.

Well, there are tools available to perform text processing. They leverage machine learning and natural language processing (NLP) to analyze and understand text in human language and extract knowledge from it.

In machine learning applications , like spam filtering, sentiment analysis, and many others, text processing is one of the most typical tasks.

Importance

It is an underlying process used by many applications we use every day. As we are more inclined to buy products and services online, we interact with brand owners through different channels.

This text serves as a crucial aspect for businesses to extract meaningful insights. It helps them understand how customers search for products, interact with them, what improvements they need to see in a product or service, and many others. All this information helps them deliver better service or products and ensure maximum satisfaction.

However, it becomes essential for companies to organize, analyze, and sort text-based data to derive insights from it. Hence, here comes the role of text processing. It analyzes and sorts raw data to extract useful information.

Text Processing Methods

1. Statistical Methods

Math and statistics are the heart of text processing. You can use various statistical methods to analyze and process the text.

The following are the different statistical approaches:

• Word Frequency

This method highlights the text's most frequently used words, phrases, or expressions. With insights from this method, it becomes easy to address issues in the text and identify success areas.

• Collocation

This method helps identify words that commonly appear together, i.e., co-occur. A text generally consists of bigrams (two adjacent words) or trigrams (three adjacent words). The common examples of collocation are – keeping in touch , product in the launch , and application under development .

• Concordance

It helps identify how a specific word is used in different contexts in a text. In short, it aids in identifying the ambiguity in human language.

For instance, we use the word ‘issue’ to address different scenarios, such as a problem, situation, supply, or topic. Here are a few examples:

1. Problem: There is a severe issue with my health.
2. Situation: It is an issue to deal with.
3. Topic: It is a crucial issue.
4. Supply: Your order has been issued.

• TF-IDF

It is an acronym for the term frequency-inverse document frequency. It measures the importance of a word in a document. However, it is balanced by the documents containing the word.

For example, the words – ‘the’ or ‘and’ frequently appear in every document. As a result, they do not help identify topics or themes in documents. On the other side, consider a single document contains the word “RAM” multiple times. As this word is unique and found only in one document, it helps provide some meaningful information about that document.

2. Text Classification

It analyzes the content and classifies text into already-defined categories. This helps businesses automatically analyze and classify their textual content. Here are some popular text classification models:

• Topic Analysis

It interprets and divides large sets of text into different categories of topics and themes. It eliminates the need to go through thousands of reviews on your product or service and determine the most favorite or most-talked feature. An automated model takes care of it all.

To understand topic analysis, consider you own a website like Amazon. You run a survey to understand how your service is beneficial to customers and users. It is not feasible to manually go through the thousands of responses to the survey, as it is time-consuming and intimidating.

However, the topic analysis makes it a matter of seconds. You need to define different features of your service, such as UI/UX, pricing, quality, functionality, and products. The topic analysis will automatically find the feature most loved from the survey responses.

For instance, consider one of the responses:

“I love the look and feel of the website; it is straightforward to navigate.”

The topic analysis would immediately classify it under the UI/UX category.

• Sentiment Analysis

It understands the emotional undertones of the text and classifies them as positive, negative, or neutral. Whether a comment on a social media post, a response to a survey, or a product review, sentiment analysis determines whether they are negative, positive, or neutral. Typically, sentiment analysis assists companies in determining how their customers feel about their products or services.

For instance, if I tweeted about the airline – Air India, something like this:

“I loved the service, and the staff in the plane was accommodating.”

It is a positive tweet, but there would be numerous negative tweets.

With the help of a model to detect sentiments, you can sort all tweets into three categories – positive, negative, and neutral. The model significantly saves time. Also, it helps you focus on negative comments and respond to them as soon as possible.

• Intent Detection

It determines the intent or goal of the text – whether it is gaining information, purchasing a product or services, or subscribing or unsubscribing from specific services.

Through intent detection, businesses can determine where their user or lead is on their buyer’s journey.

For instance, you may have found software that piqued your attention after reading all the information. You would undoubtedly want to learn more about the application, so send the following text to its provider:

“The software you offer interests me a lot and is useful. I would like to know if it offers more affordable packages?”

Intent detection classifies the above text as a Request for Information .

Let us look at another example: You buy certain accessories online from a website for your dog, and you love the products and continue using them. You would email them to get added to their newsletter to receive coupons and offers.

“Thank you for providing an amazing experience for my fur baby. I would love to be part of the newsletter to receive events, coupons, and offer.”

Intent detection will classify the email as Subscribe to Newsletter .

• Language Classification

It uses language as a basis to classify the text.

Let us understand this with an example. Imagine you are an online retailer, making your products available worldwide. It is obvious to receive customer support tickets from different countries in different languages.

A language detection classifier classifies customer support tickets based on the languages.

The best example of this is Amazon. It is the best eCommerce platform, receiving support tickets from people from different countries. A language detection classifier sends the support tickets to the respective teams.

3. Text Extraction

It is the process of identifying valuable data from the given text. It helps find important information, such as client names, product details, prices, dates, keywords, and other valuable information.

• Keyword Extraction

It automatically determines and extracts significant words and phrases from the text.

For instance, we generally add hashtags while we tweet or post on Instagram. Consider a cricket league. Fans will tweet about the tournament, players, etc.

The keyword extractor will analyze the hashtag expressions and determine which team in the tournament or player is most-loved.

• Entity Extraction

It automatically identifies and obtains the name of companies, brands, people, etc., from the text.

One best example of entity extraction is determining which branches of a company receive good and bad feedback.

Text Processing Use Cases or Applications

Customer feedback and customer service are two primary text processing applications.

1. Customer Surveys and Reviews

With text processing, companies can analyze customer responses and reviews of customers. They can classify their customers as promoters, passives, or detractors based on their responses to specific products or brands. In short, companies can determine the success of customer retention.

In survey data, topic classification can determine the topics common among customers, a keyword extractor helps identify commonly-used words or phrases, and sentiment analysis to understand the perspective of customers about a product or service.

2. Support Tickets (Customer Service)

Large companies operating worldwide often allow customers to submit support tickets. So, people from different regions of the world submit support tickets in different languages. With text processing, companies can identify the ticket topic and its urgency and send it to the customer service representative that speaks the same language as the customer. When done manually, it is a time-consuming process.

Conclusion

Text Processing has been a boon to companies and businesses as it manages data and customer experiences, which are the lifeblood of any company.

Automating the analysis of unstructured data and deriving valuable insights help companies understand their customers’ perspectives about products or services. This helps them improve their brand and make informed decisions, leading to a company's growth in revenue and reputation.

Please let us know in the comments section if you have any queries regarding this article.

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