Keyword Extraction is the process of extracting the most important word or expressions from the text. It is also known as Keyword analysis or keyword detection. It will help you recognize the topic discussed and summarize the content.
Keyword analysis uses artificial intelligence (AI) with natural language processing (NLP) to break down human language to be understood by the machine. It is used in almost everything that is published online, like blogs, social media comments, news reports, reviews and more.
Let us look at the example for this. Suppose you want to go through the reviews on your product; keyword extraction here will help you shift the whole set of data and extract the words that best describe each review in seconds. It uncovers the most critical attributes.
This article will mention the importance of keyword extraction and how it works simply to help you understand what it is.
Why is Keyword Extraction Important?
Considering that over 80% of the data we generate daily is unstructured, i.e. that they are not organized in a predefined way and are, therefore, difficult to analyze and process, keyword extraction is terrific. It is a powerful tool that can help you understand data about a page, customer reviews, and comments. In short, any unstructured data.
Some of the great benefits of keyword analysis include the following:
You can perform keyword extraction on social media posts, customer reviews, customer support tickets and much more to get insight into what’s being said about your product in real-time.
In other words, keyword extraction helps extract relevant information from a large amount of unstructured data. For example, you can understand the essential terms in a text and the topics covered by extracting keywords or phrases.
Now that you know the keyword extraction concept and how to use it, it’s time to understand how it works.
The following section explains the essentials of keyword extraction and introduces you to the different approaches to this method, including statistics, linguistics, and machine learning.
Automated keyword extraction lets you evaluate as much data as you want. Sure, you could read the texts and identify the key terms manually, but that would be time-consuming. So instead, automating this task gives you the freedom to focus on other tasks.
Keyword extraction works based on predefined rules and parameters. You won’t get any inconsistencies. The latter are frequent when the analysis of the text is manual.
How does Keyword Extraction Work?
Keyword extraction makes identifying relevant words and phrases from unstructured text easy. This includes web pages, emails, social media posts, instant messaging conversations, and any other type of data that is not organized in a predefined way.
There are different methods you can use to extract keywords automatically. From simple statistical approaches that detect keywords by counting word frequency to more advanced approaches made possible by machine learning, you’ll be able to set up the model that fits your needs. This section will examine different keyword mining approaches, focusing on machine learning-based models.
Simple Statistical Approaches
Using statistics is one of the easiest ways to identify keywords and phrases in the text. Statistical approaches include word frequency, word collocations and co-occurrences, TF-IDF (term frequency-inverse document frequency), and RAKE (Rapid Automatic Keyword Extraction).
These approaches do not require training data to extract the most important keywords from a text. However, since they are based on statistics, they may overlook relevant words or phrases that are only mentioned once. So, let’s take a closer look at these different approaches:
Word frequency lists the words and phrases most often appear in a text. This can be very useful for multiple purposes, from identifying recurring terms in a series of product reviews to finding the most common issues in customer service interactions.
However, approaches based on word frequency consider documents as a simple “collection of words”, leaving aside crucial aspects related to semantics, structure, grammar and word order. Synonyms, for example, cannot be detected by this method.