Text Processing with AI

Text processing is an artificial intelligence (AI) technique that employs natural language processing (NLP) to convert the unstructured text in documents and databases into normalized, structured data that can be analyzed or used to get insights, drive decision making and automate business processes.

In text-processing, unstructured text data is scrutinized and sorted to gather valuable insights. Text processing tools can comprehend human language automatically utilizing natural language processing (NLP) and machine learning, two subfields of artificial intelligence.

You accept raw text data through emails, PDF documents, chat discussions, social media, and other channels because we inherently interact in words, not numbers. This unstructured data has thoughts and perspectives on many topics, goods, and services, but you must first organize, analyze, and measure textual data to access applicable information.

Your customer assistance teams can have text-processing expertise to automate the activities such as ticket labeling and routing. Also, your product teams may use it to glean insights from customer comments to assist them with their product roadmap. The AP team can process pdf invoices and match with other documents before processing payment. In many operations, your team may handle large numbers of documents that comes through email and this is a time consuming process that can be automated using text processing AI.

Document data extraction

Document processing AI

We can use intelligent document processing or IDP to extract relevant data from unstructured documents such as PDFs. This AI technology allows us to automate processes like manual data entry, document processing, document comparisons and other document intensive processes across the departments.

Analyze and Sort your unstructured data using the following techniques and tools:

Extraction of text

Extraction of text is one of the text processing approaches that discovers and extracts relevant data from a phrase. Text extraction can extract keywords, client names, product details, dates, prices, and any other information as needed from data.

Extraction of text involves two more methods such as:

Keyword Extraction: relevant words or expressions in the text are automatically detected and extracted using the keyword extraction technique.
Entity Extraction: extraction fetches names of persons, companies, brands, and other entities automatically. It is useful when you want to identify competitors, brands, and people that have a significant impact on your business. You may utilize entity extraction to determine which corporate branches get positive or negative feedback.

Classification of Text

By classifying the text based on its content, organizations can analyze and sort textual data automatically. Some of the most popular text classification models include topic analysis, sentiment analysis, intent detection, and language classification.

Topic Analysis: This technique analyzes large collections of texts and categorizes them according to specific topics or themes.
Sentiment Analysis: sentiment analysis detects customer reviews, survey responses, social network postings, and other psychological elements. It helps businesses gain a deeper understanding of their clients' attitudes toward their brand, product, or service.
Intent Detection: This method automatically detects the purpose, goal, or intent of any text. Users or leads can find out exactly where they are in the buyer's journey.
Language Classification: Classifying text based on language is the function of language detection models.

Statistical Methods

To process and analyze text, you can use statistical methods that include frequency distribution, collocation, concordance, and TF-IDF.

Collocation: This strategy aids in the identification of terms that frequently appear together. The most prevalent collocations in the text are bigrams (two adjacent words) and trigrams (three adjacent words).
Word Frequency: This statistical method identifies the words or expressions that appear most frequently in a given piece of text. You may address troublesome situations, find areas of achievement, and more with this unique knowledge.
Term Frequency-Inverse Document Frequency: This metric determines the significance of a word in a document, but it is polluted by the number of documents that contain the word.
Concordance: Concordance is about providing context. By analyzing the use of words in different contexts, this method decodes ambiguity in human language.

What is the Essence of Text Processing

The majority of individuals are unaware of text processing, but most of us use apps that utilize text processing every day.
Text data is one of the essential ways for businesses to derive business insights, especially since our interactions with brands have grown increasingly online and text-based.
Text data may reveal how customers look for, buy, and engage with a company's brand, products, and competitors on the internet. Intelgic's text processing expertise combined with machine learning enables businesses to deal with massive amounts of text data.

What Commercial value could text processing provide?

Text processing enables businesses to automate procedures that yield meaningful insights, allowing you to make better decisions. Automated text processing can dramatically improve the customer experience.

Reviewing and conducting surveys

With text processing, companies can identify customers as promoters, passives, or detractors based on responses to open-ended questions about a brand.
You can use this data to determine how successful your customer retention program is and check what offers or information clients will receive from the brand.
Keyword extractors look for specific keywords within customer responses and topic classification to determine which areas customers are interested in and apply sentiment analysis on top of it.
Determine what percentages of customers are optimistic, pessimistic, or unenthusiastic about the brand.

Supports Tickets

Most businesses allow customers to submit customer service tickets online.
For companies that operate across the globe, it is common to use the text-processing for customer service.
With this text-processing; you can recognize the topic of a ticket, determine the urgency, and send the tickets to a customer service representative that understands the human language. Handling this manually would take too much time without machine learning.
With the help of text processing and other machine learning models, you can leverage its data and customer experience.
Your ability to make decisions depends on accurate analysis and valuable data, which is possible with machine learning.
Since you can get accurate insights about almost anything using data, you have no reason to be constructing uninformed conclusions.