6 Must Know Natural Language Processing (NLP) Techniques

We know that computers are good at crunching numbers, but they still struggle with language. This is where Natural Language Processing (NLP) comes in.

NLP helps computers understand language by using machine learning algorithms to analyze text and identify patterns and relationships within it.

Over the years, NLP has revolutionized data analytics and helps businesses across all industries understand what their customers want and how they want it so that they can deliver customer satisfaction.

But let’s first understand:

What exactly is Natural Language Processing?

Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages.

The goal of NLP is to develop computational models that can accurately process and understand human languages.

There are many different approaches to NLP, but all share the same basic goal: to develop algorithms that can automatically process and understand human language.

Why is NLP important?

NLP techniques can be used in many different ways, but they're most often used for helping businesses communicate with their customers. NLP can help companies understand what their customers want and how they want it, so that they can deliver customer satisfaction.

The importance of NLP will only continue to grow as we increasingly rely on technology in our everyday lives.

6 Natural Processing Techniques

Here are the 6 most used NLP techniques:

1. Stemming and Lemmatization
2. Sentiment Analysis
3. Named Entity Recognition (NER)
4. Bag of Words (BoW)
5. Keyword Extraction
6. Topic Modelling

1. Stemming and Lemmatization

Stemming and Lemmatization refers to the process of Ai machine learning recognizing and tagging words based on their stems and/or definitions. The word ‘Lemmatization’ comes from linguistic studies and is rooted in ‘lemma’ which means the canonical form.

This process is done by removing the inflection of the word and returning to its ‘dictionary form’: also known as the morphological analysis of a word.

In an easier sense, the process of stemming when being used by search engines, chatbots, and AI uses the stem of the word, while lemmatization also works around the context.

An example of this would be allowing machine learning to understand the difference and the similarity between true and truth.

2. Sentiment Analysis

Sentiment analysis is a form of machine learning that can be used to analyze text and extract sentiments from it.

The goal of sentiment analysis is to provide insights into the emotional state of a user and can be used to collect customer insights which is further leveraged to gauge customer satisfaction and loyalty.

Some of the use cases of sentiment analysis include different areas of a business such as customer support, social media analysis, customer reviews, etc.

Examples of some use cases:

• Marketing: Sentiment analysis can help us understand how our customers feel about our products or services. We can use this information to customize marketing campaigns based on which topics are being talked about most among our audience; we can also use it to develop new products that appeal to different types of customers and see which ones do well in terms of sales numbers.
• Customer support: Sentiment analysis can help us better understand why customers are complaining about something in a way that will allow us to fix the issue quickly and efficiently—so we don't waste time dealing with complaints that won't lead anywhere useful anyway!

3. Named Entity Recognition

Named Entity Recognition or NER is one of the more popular NPL used by companies. In machine learning, Named Entity Recognition is considered best to deal with proper nouns such as company, organization, or individual names.

NER tags ‘named entities’ within a text and extracts these for analysis. NER can be used for:

1. CX and User Analysis: Understanding customer feedback and the buyer journey can be made a seamless experience by using NER in the right way. Keeping an eye out for repetitive patterns or names can help you identify pain points much faster as NER extracts their mentions from the vast sea of data you receive.
2. Powered Content Recommendation: For media companies like Disney+ and Hulu, NER helps in automating the content recommendation cycle. Understanding, analyzing, and picking out your next recommendations is not a randomized task. As users, the fact that a large percentage of new content ‘discovered’ is through the recommended section makes NER a very relevant and important tool in the digital age.
3. News Cycle Analysis: NER can help analyze and articulate for how long a news item stays in the news cycle, and inadvertently in popular consciousness. This can be used to understand item relevance and push algorithms.

4. Bag of Words (BoW)

Bag of words is a statistical technique used to count the number of unique words in a set of text. It's important because it helps us understand how often certain words are used, and how they interact with each other.

The bag of words is a statistical model that helps you identify what words are most likely to be used in a given context. The idea is simple: if you know the frequency of each word in your data, you can calculate which words are more likely to be used together.

This allows you to find patterns and relationships between words that may have been missed by traditional methods.

The bag of words model uses a corpus to determine which words are present in the text. This can be done by breaking down the text into sentences and determining how many times each sentence appeared in the corpus. Because this method only finds unique words, it can be used to count both nouns, verbs, and other words.

5. Keyword Extraction

Keyword extraction, also known as keyword analysis or keyword detection, is a machine-learning technique that allows for the summarization of a large volume of text data by extracting important keywords.

In large data sets, keyword extraction can be used to recognize relevant and important takeaways and aid in uncovering significant information or issues.

Keyword extraction uses AI to run through large documents, online forums, news reports, press releases, social media comments, and more to filter out the most pertinent words cropping up reportedly about you or your brand. 80% of the data generated is unstructured, and hence extremely difficult to comb through for analysis. In data science, keyword extraction is hence considered a very important tool to ensure all rounded and time-efficient analyses.

Automated word clouds (or tag clouds) are a great example of keyword extraction.

6. Topic Modelling

Topic modelling is a method of analyzing text for the purpose of identifying and extracting relevant words, phrases, and even entire sentences.

It can be used to identify topics in text and extract them from the mass of data. Topic modelling is important because it allows us to sort through large amounts of information, find what we are looking for, and make connections between seemingly unrelated things.

For example, if you wanted to learn about the topic of "chocolate", you could look at all the documents that contain "chocolate" in them to see if any of them have anything else that's related to chocolate.

You'd probably find one or two. The next step would be to start looking at those documents and seeing what words they have in common with "chocolate". If you did this enough times, eventually you'd find topics, such as "cookies", "fudge", etc., which are all related to chocolate, but not necessarily about it directly.

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