Are text analytics and natural language processing (NLP) the same thing? The short answer is no, but they are closely related.
NLP plays a critical role in enabling effective text analytics, preparing data and laying the groundwork for powerful insights.
NLP: The Engine Behind Text Analytics
If text analytics is a car, then NLP is the engine. Text analytics transforms text data into actionable insights. To do this, we must understand the meaning of the text, not just identify the frequency of specific words. This is where NLP comes in.
Natural language processing is a branch of artificial intelligence that uses machine learning to help computers understand human language. In the context of text analytics, NLP is an invaluable tool for cleaning unstructured text data and ensuring it is ready for analysis. This allows market researchers to analyze more text data with greater accuracy—truly a win-win.
The process begins by preprocessing the data through tokenization (breaking sentences into individual words or phrases) and removing stop words that do not add significant meaning to a sentence (like "for" and "with"). Once the text is preprocessed, it is ready for machine learning models to interpret.
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NLP and Text Analytics: A Powerful Combination
It’s important to note that both text analysis and NLP exist as techniques in their own right. Text analysis refers to the process of extracting insights from unstructured text data, while NLP is a specific branch of AI and is the core technology behind everyday applications, such as autocorrect on your phone and virtual assistants like Siri and Alexa.
However, together, NLP and text analytics form an unrivalled insight-generating machine. When analyzing text, the many quirks of human language—such as words with multiple meanings—can complicate the extraction of meaning. This is why prior to the advent of NLP, text analysis was such a time-consuming task. By combining NLP with text analytics, computers can understand text like humans do, picking up on nuances and comprehending phrases and their meanings. This dramatically improves the depth of understanding and reduces the manual effort previously involved in text analytics.
Try Text Analytics for Yourself
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