NATURAL LANGUAGE PROCESSING

Everything we express carries a huge amount of information. Generally, we express in words of some language. But it is not limited to only that; importance should also be given to parameters like thoughts, context, tone and word of choice.

These parameters make data more informative, from which valuable insights could be obtained. Typically, it falls under unstructured data, where we cannot find patterns directly. Some of the examples are data generated from conversations, tweets, etc.

Unstructured data contains a lot of flaws and disarrangement. Nevertheless, with advancements in technology, particularly in areas like machine learning, nowadays, it is no longer about trying to interpret text or speech based on its keywords (the old-fashioned way) but about understanding the meaning or real emotion behind those words (New cognitive way).

Natural Language Processing (NLP) is a field of artificial intelligence that allows machines to read, understand and derive meaning from human languages.

In simple terms, NLP represents the automatic handling of natural human languages like speech and text. Although the idea itself is intriguing, the technology’s use cases are where its actual value lies.

The ultimate goal of NLP is to enable computers to comprehend languages in a manner similar to that of the human brain. It is the driving force behind virtual assistants, speech recognition, sentiment analysis, automatic text summarisation, machine translation and many more.

Below are some of the use cases of NLP:

i) Vectorisation

ii) Sentiment Analysis

iii) Named Entity Recognition

iv) Summarisation

v) Topic Modelling

vi) Text Classification

vii) Keyword Extraction

viii) Lemmatization and stemming

SOURCES OF TEXT DATA

The first step for every natural language processing algorithm starts with data collection. Some of the significant sources of text data are gathered from social media platforms, online news channels (tweets, comments, reviews,

sentiments, news, etc.)

Common challenges we face during analysing textual data

As discussed in the introduction, we found that text includes more information than just a collection of words. We will discuss some of the challenges that organizations face quite often.

Text or sentiment analysis could be difficult using natural language processing simply because machines have to be trained to analyse and understand emotions like a human brain does.

This is in addition to understanding the nuances of different languages. The most commonly faced challenges are:

1.Tone

Tone is very useful while analysing human behaviour and feedback. More complications are added when we analyse a massive collection of data that could contain both subjective and objective information.

2. Sarcasm

We use a single word in different situations; some expressions could have multiple meanings. Most commonly, in casual conversations, people use irony and sarcasm.

It is difficult for sentiment analysis tools to understand or detect the proper context of the expression if a negative sentiment is expressed.

3. Emoticons

In the era of the 20th century, emojis are used more than text. To express every emotion, we can use emojis on social media handles. Generally, NLP tasks are trained explicitly for languages. While they could extract textual data from images, emojis are a language in themselves.

Most emotion analysis solutions treat emojis like a unique character that is removed from the data during text mining, resulting in inaccurate data analysis.

How NLP can be leveraged in the insurance domain: –

1. Customer Services

The real success of any business is based on how satisfied the consumers are with their goods or services. It was found in a recent survey that more than three-fourths of insurance customers wanted personalized offers, messages, pricing and recommendations.

2. Managing Claims

The main objective of any customer taking insurance is to secure him/her or his/her family members from unexpected medical expenses. Customers always expect their insurance provider to process their claims quickly and efficiently.

Insurance firms could use NLP models during phone calls to recognise customers’ speech and automatically collect the required information.

3. Suggesting Rejection Categories

Claim processing team has to choose a relevant rejection category to reject a claim. There could be a lot of rejection categories available in the system, and to select a particular category manually may involve plenty of human errors. NLP could help in suggesting the most relevant categories based on the contextual meaning of rejection remarks.

4. Detecting Fraud

Many traditional and experimental technologies have been leveraged in combatting insurance fraud, and no wonder NLP can contribute towards Fraud detection too.

Using topic modelling, rejected claims can be segregated into relevant rejection categories based on the remarks given by claim processors, and it can further be utilised to identify fraudulent claims.

Keywords Extraction and Sentiment Analysis

The automatic retrieval of targeted information about a chosen topic from multiple sources is known as Information extraction (IE). With the aid of information extraction technologies, data can be retrieved from text documents, databases, websites or a variety of other sources.

Sentiment analysis often referred to as opinion mining, is a natural language processing (NLP) method for identifying data’s positivity, negativity or neutrality.

Customers’ health insurance data can be analysed and filtered through the support of Information extraction (IE) and Sentimental Analysis (SA). Therefore, Insurers can efficiently track the data of Policyholders and can capitalise it effectively for future businesses.

Summary

NLP is a very useful technique which would help the firm to know more about their customers’ satisfaction and feedback about products and services.

Thank you for reading the article. We will be back with some more exciting topics in the field of data science.