Text Analysis And Machine Learning Models

 

What is text analysis?

Text analysis is the practice of reading and comprehending human-written text and using computer tools to gain business insights. Software for text analysis can autonomously categories, analyze, and extract data from text to find trends, connections, sentiments, and other useful information. Text analysis can be used to quickly and accurately evaluate a variety of text-based sources, including emails, documents, posts on social media, and product reviews, just like a human would.

Why is text analysis important?

Text analysis is used by businesses to glean useful information from a variety of unstructured data sources. For decision-making support, they rely on feedback from sources including emails, social media, and customer surveys. Without anyone helping with text analytics, the enormous amounts of Text dataset from different sources become overwhelming.

You can more quickly obtain reliable information from the sources with text analysis. IT presents actionable data and is entirely automated and consistent. Using text analysis software, for instance, enables you to quickly identify unfavorable sentiment on social media posts so you may take action to address the issue.

Analysis of feelings

To comprehend the opinion expressed in a text, sentiment analysis and opinion mining employ text analysis techniques. To ascertain whether your customers are pleased with their purchases, you can employ sentiment analysis of reviews, blogs, forums, and other online media. You can identify new trends, monitor sentiment changes, and address PR issues with the use of sentiment analysis. You can follow changes in consumer opinion and pinpoint the problem’s main source by employing sentiment analysis and detecting particular keywords.

Management of records

Effective document management, classification, and searches are made possible by text analysis. This involves automating the management of patient records, keeping track of brand references, and spotting insurance fraud.

Modifying the client experience

Emails, reviews, conversations, and other text-based correspondence can all be processed using text analysis tools. You can create customized experiences for various consumer categories by using knowledge about customers’ preferences, purchasing patterns, and general brand perception.

How does text analysis work?

In order to interpret the semantic context of unstructured data, text analysis algorithms must first be trained to correlate words with particular meanings. Connecting words to things, things to do, and things to feel, is comparable to how people learn a new language.

Deep learning and natural language processing are the guiding principles behind text analysis software.

Deep learning

Data science’s field of artificial intelligence teaches computers to think like people. An approach used in artificial intelligence called machine learning employs particular techniques to instruct or train machines. Deep learning is a highly specialized form of machine learning that makes use of artificial neural networks or brain-like software architectures. Text analysis software is powered by deep learning technology, enabling these networks to interpret text similar to the human brain.

NLP (Natural Language Processing)

A subfield of artificial intelligence known as “natural language processing” (NLP) enables computers to automatically infer meaning from naturally occurring, human-written material. The deep learning system is trained to interpret and evaluate text data, including handwritten text images, using linguistic models and statistics. By locating and comprehending the words in the photos, NLP techniques like optical character recognition (OCR) transform text images into text documents.

What are the types of text analysis techniques?

These conventional methods are applied by the text analysis programmer.

Text categorization

Text categorization is the process through which text analysis software learns to relate particular keywords to particular topics, user intentions, or attitudes. It accomplishes this using the following techniques:

• Rule-based classification tags the text according to established guidelines for syntactic or semantic elements.
• With the help of examples, machine learning-based systems train text analysis software to tag texts more accurately. To analyze structured data, classify words, and create a semantic relationship between them, they make use of linguistic models like Naïve Bayes, Support Vector Machines, and Deep Learning.

For instance, terms like good, rapid, and great are frequently used in positive reviews. However, unfavorable reviews could include phrases like “unhappy,” “slow,” and “poor.” Data scientists teach text analysis algorithms to hunt for these particular terms and classify reviews as favorable or unfavorable. The customer service personnel will then be able to readily track consumer feedback from the reviews.

Text extraction

Key information is extracted from the text through text extraction from AI training datasets. It can find words like “keywords,” “product attributes,” “brand names,” “location names,” and more in a text. The following techniques are used by the extraction software:

• REGEX or regular expressions is a prerequisite for what needs to be extracted and is an array of symbols that have been prepared.
• CRFs (conditional random fields): By analyzing particular patterns or words, this machine learning technique extracts text. Over REGEX, it is more sophisticated and adaptable.

For instance, text extraction can be used to track brand mentions on social media. It is impossible to manually monitor every mention of your brand on social media. You will receive a real-time notification when your brand is mentioned in the text.

Modelling a topic

A topic or theme is created by using topic modelling methods to find and group similar terms that appear in an unstructured text. Based on the frequency of specific words in the text, these algorithms can read several text documents and group them into themes. Methods for topic modelling provide context for further document analysis.

For instance, you may look through your scanned document library and categorise the items into invoices, legal documents, and client agreements using topic modelling techniques. Then you can use various analysis techniques on bills to learn more about finances or on customer agreements to learn more about customers.

PII redaction

In a document, personally identifiable information (PII) like names, addresses, or account numbers is automatically found and removed using PII redaction. In addition to preserving privacy, PII redaction complies with regional laws and regulations.

Before indexing the documents in the search solution, for instance, you can search through support requests and knowledge base articles for PII and remove them. After that, PII in documents is not present in search solutions.

Text datasets and GTS

Collecting and analyzing text data is not an easy task. But thanks to Global Technology solutions, their expertise and experienced team take care of it all. Data collection and annotation are among their best services. The data collection like image data collection, text, video and speech data collection.