Keyword extraction process with Natural Language Processing

September 13, 2022

The first thing you do when you get up in the morning is open your phone and check your messages. Your mind has been conditioned to avoid WhatsApp communications from individuals and groups you dislike. The message's importance is determined solely by the keywords of persons and group names. 

The same behavior can be replicated via machine learning. In Natural Language Processing  (NLP), this is referred to as keyword extraction. Reading articles or news will therefore be influenced by extracted keywords such as data science, machine learning, artificial intelligence, and so on. Not only does the keyword extraction technique separate the content, but it also saves time on social networking sites. You can decide whether to read the post or the comments depending on their keywords.

In this blog, we will be briefing you on what keyword extraction is, why is it important, how can you do it through NLP, and will discuss its advantages through the end of the blog. 

What is Keyword Extraction

Keyword extraction is a technique that is widely used to extract important information from a sequence of paragraphs or texts. Keyword extraction is a way of extracting the most relevant words and phrases from text input that is automated. It is a text analysis method that extracts the most essential words and expressions from a page automatically. It aids in summarizing the content of a work and identifying major subjects being tackled.

Machine learning artificial intelligence (AI) and natural language processing (NLP) is used in keyword extraction to break down human language so that it can be interpreted and evaluated by machines. It is used to extract keywords from a wide range of material, including conventional documents and business reports, social media comments, internet forums and reviews, news items, and more.

Why is keyword Extraction Important

With keyword extraction, you may quickly locate the most essential terms and phrases in large datasets. And these terms and phrases can provide useful insights into the themes your clients are discussing. Given that more than 80% of the data we generate every day is unstructured - that is, it is not organised in a specified fashion, making it exceedingly difficult to evaluate and process - businesses require automated keyword extraction to help them process and analyse consumer data more efficiently.

Assume you wish to examine thousands of online product reviews. Keyword extraction allows you to quickly filter through a large amount of data and extract the words that best describe each review. As a result, you can easily and immediately identify what your customers are talking about the most, saving your employees hours and hours of manual processing.

Whatever your industry, keyword extraction tools are essential for automatically indexing data, summarising a text, or creating tag clouds with the most representative keywords.

How to Extract Keywords using Natural Language Processing

Natural Language Processing (NLP) is the best option to gain a high-level understanding of the overall tenor of the dataset, then use that understanding to identify more focused lines of inquiry—either to apply to the data itself or to guide the related study. A wide range of free Python NLP modules provides some reasonably simple-to-implement algorithms for uncovering significant aspects of huge datasets.

1. Load the dataset and identify text fields to analyze

First load the data .csv or .tsv file, select the column containing the data you wish to examine, and then you will evaluate the most and least common words in the unprocessed text. These will assist you in identifying any custom stop words that you may choose to include before normalising the text.

2. Create a list of Stop Words

Stop words are regularly used words such as "the," "a," "an," "in," and so on that occur frequently in natural language but do not provide important information about the meaning or subject of a message. The NLTK module provides a list of the most common English stop words, which you can import. One can also provide a list of bespoke stop words based on the text that they are examining. A list of "most often occurring words" provides some good choices for designing the custom stop words list. 

3. Pre-processing the data to get a cleaned and normalized text corpus

Pre-processing entails removing punctuation, tags, and special characters from the text before normalising what remains into identifiable words. Normalization involves "stemming", which eliminates suffixes and prefixes from word roots, and "lemmatization", which maps the remaining root forms (which may or may not be proper words) back to a natural language word. All of these procedures identify a canonical representative for a set of related word forms, allowing us to estimate word frequency independent of morphological (word form) variances.

4. Extract the most frequently occurring keywords and N-grams

We've now arrived at the point where we can build a list of top keywords and n-grams, in this case, two and three-word phrases (bigrams and trigrams). These lists and charts, of course, barely scratch the surface of the information that could be found in this text corpus, but they do point us in the right direction for further investigation. They also provide a high-level summary that partners and stakeholders may easily understand.

5. Extract a list of top TF-IDF terms

The TF-IDF statistic, which stands for "Term Frequency-Inverse Document Frequency," is a numerical statistic that measures how relevant a word is to a document in a collection. The TF-IDF value of a term grows in proportion to the number of times the word appears in a document and is then offset by the number of documents in the corpus that contain the word. This compensates for the fact that some words appear more frequently than others. As a consequence, we have a list of words rated by how significant they are to the corpus as a whole.

Advantages of Keyword Extraction

The benefits of keyword extraction are numerous, but we have narrowed them down to three.

Scalability: You can analyse as much data as you want with automated keyword extraction. Yes, you could manually read texts and identify key terms, but it would take a long time. By automating this work, you will be able to focus on other aspects of your profession. 

Consistent criteria: Keyword extraction operates on the basis of rules and established parameters. Inconsistencies, which are typical in manual text analysis, are avoided. 

Real-time analysis: You can execute real-time keyword extraction on social media postings, customer reviews, surveys, or customer support issues to gain insights into what's being said about your product as it happens and track it over time.


The keyword extraction procedure aids us in discovering crucial terms. It is also useful for subject modelling jobs. With just a few keywords, you can learn a lot about your text data. These keywords can help you decide whether or not to read an article. It is already used in some of the major fields/industries out there. Including Social media monitoring, Brand monitoring, Customer service, Customer feedback, Business intelligence, Search engine optimization (SEO), Product analytics, and Knowledge management.

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