Keyword extraction, often known as keyword detection, is a text analysis technique that extracts keywords from the text. People usually use it to summarize enormous quantities of data to identify the vital points of discussion.
Natural language processing (NLP) and machine learning are used in keyword extraction to break down textual data and make it machine-readable. You can use it to extract important words from unstructured data such as documents, reports, online comments, news, and social media information.
Analyzing client feedback is a typical use case for keyword extraction. Brands and organizations must sift through thousands of reviews to determine what customers like and dislike. Keyword extraction can assist you in analyzing client feedback and extracting keywords that phrase from reviews. As a result, you can gain access to insights such as customer opinions or brand perception.
What Are the Benefits of Using Keyword Extraction?
Businesses today generate a massive amount of data, the majority of which is unstructured. Because the data is unstructured, analyzing it becomes incredibly challenging. However, keyword extraction can find essential keywords and phrases in large databases. It allows you to summarize the data with ease and derive vital insights.
Keyword extraction can be a beneficial tool for researchers and academic students looking for relevant information in large amounts of unstructured data. They can automate indexing, summarizing, and extracting vital keywords from large amounts of data.
Here are some other benefits of keyword extraction:
- Scalable: Keyword extraction allows you to parse and analyze massive amounts of text data. You can automate the recognition of key phrases and other information. It reduces the time required to process the data when compared to doing it manually.
- Reliable: There is always the possibility of human error while manually analyzing vast amounts of unstructured material. However, keyword extraction operates on data according to predetermined rules. There is no chance of inaccuracies while analyzing the data.
- Real-time Analysis: You can process data from various sources, including social media, reviews, surveys, and so on. The voice of customers’ solutions is an outstanding example of this. It is possible to automate the categorization of customer support tickets. Keyword detection can categorize tickets based on predetermined parameters and reduce response time, hence, improving your customer support services.
What Is the Process of Keyword Extraction?
Let’s take a look at the various keyword extraction methods:
To analyze and extract important keywords statistical techniques do not require training data. However, due to their reliance on statistical data, these approaches may only consider keywords that appear several times. It may overlook terms that are significant yet only appear once.
- Word Frequency: This approach identifies words and phrases that appear multiple times in unstructured data.
- Word Collocations: This technique lists terms that go together and appear in the data several times. Customer service, email notifications, customer experience, and so on are examples.
- TF-IDF (term frequency-inverse document frequency): This technique determined how many times a word appeared in the data. It then determines the significance of those words based on the number of times they appear.
- RAKE (Rapid Automatic Keyword Extraction): This technique identifies the most relevant keywords from unstructured data using a list of stopwords and phrase delimiters.
The linguistic approach makes use of information connected to the text and the words it contains. Parts of speech, relationships between words, discourse markers, semantic information, and word occurrence are all part of the information. Most keyword extraction methods employ some form of linguistic technique to improve output accuracy.
The graphical approach graphs text in various ways. Words are composed of vertices joined by a directed edge (the one-way connection between vertices). Those edges in the dependency tree can be identified as the words’ relationship.
Machine Learning Methodology
Machine learning is an artificial intelligence subfield that creates algorithms that can learn from examples. Textual data is converted into vectors by machine learning systems (collection of numbers with encoded data). These vectors contain the various data-representation aspects.
Conditional random fields are a popular machine-learning-based keyword extraction methodology (CRF). A text-based pattern recognition system that learns patterns by balancing various properties of words in a text. This method evaluates context and variable relationships to make predictions.
A hybrid system is a combination of two or more methodologies for developing a keyword extraction model. The main reason for this is to improve the system’s accuracy.
Keyword Extraction Applications
Now that you understand what keyword extraction is and how to use it, let’s have a look at some of its most common applications:
Customer service is critical to improving the customer experience with your product or service. Most clients are willing to pay a few additional dollars for better customer service. But how can you enhance your customer service when you’re dealing with a flood of support tickets every day?
The text in these customer support tickets can be analyzed and classified using keyword extraction based on the issue or inquiry. You can identify consumers who require immediate care and shorten your response time.
Analysis of Customer Feedback
Customer feedback is a treasure of crucial information. However, personally reviewing this data can take a long time and result in missed opportunities. From consumer feedback, keyword extraction can assist you in identifying pre-defined keywords or phrases. Instead of reading them all at once, you can focus on the crucial ones. It can help you identify aspects of your product or service that need improvement along with those they enjoy the most.
Obtaining Business Intelligence
Keyword extraction can also aid in the extraction of valuable insights for business intelligence. You can use keyword extraction to compile data from numerous sources. You may analyze a product by looking at the feedback it receives. Similarly, you can examine your competitors’ product feedback. The insights can help you create a more effective product or improve an existing one.
Monitoring of Social Media
Users’ primary channels for expressing their opinions are now social media sites. Conversations on social media, on the other hand, move at a breakneck pace. Manually analyzing this data will take too much time and result in missed opportunities. Keyword extraction can let you easily assess this data and determine the most frequently discussed topics. This can help you better understand your target audience, improve your services, and respond to concerns more promptly.
SEO Search Engine Optimization (SEO)
The practice of identifying the most relevant and strategic keywords to increase the exposure of your content is known as search engine optimization. While numerous software solutions can assist you with keyword research, you can also use keyword extraction for the same. It can help you locate the most frequently used keywords and phrases in data about any topic. You can use these keywords to increase the SEO authority of your content.
Keyword extraction is one of the most efficient methods of collecting crucial information from unstructured text. It can dramatically minimize the time firms need to spend analyzing data. It can help you improve customer service, analyze feedback, monitor social media, and gather business intelligence. You can make well-informed selections without wasting time.
BytesView is a text analysis solution that offers pre-built keyword extraction tools and API. Sentiment analysis, intent identification, entity extraction, and more functions are also available. Enter the realm of sophisticated machine learning-based solutions that can help you surpass your competitors.