Natural language processing, also known as NLP, is a technique used by software to understand meanings, sentiments, attitudes, and other insights from text.

For example, a computer will use natural language processing to understand the meaning and sentiment in the sentence “Jonathan binge watched one season of The Walking Dead on Netflix over the weekend” and provide insights to analysts.

To understand the text, the computer would have to know that Jonathan is a noun, watched is a verb, etc. This approach is called lexical analysis or part-of-speech tagging.

Then, the computer would need to determine the structure of the sentence. For example, the computer needs to figure out that Jonathan is taking an action (watched) on a season of The Walking Dead. More, the computer can perform a semantic analysis to understand the relationship of the sentence. For example, a computer can use keywords such as “watched” “binged”, “The Walking Dead”, and “weekend” to understand that Jonathan has been watching a lot of The Walking Dead on the weekend. Then, the computer can make inferences of the situation. For example, the computer can infer that Jonathan was relaxed and had lots of spare time over the weekend which granted him plenty of time to watch a whole season of The Walking Dead in two days.

These analysis help business owners to understand online text and gain insights to their customers’ feedback, sentiment, and more valuable information.

While these processes and analytical approaches appear straightforward and simple, NLP is facing several challenges:

1. Word ambiguities

   For example, the word silver can be used as a noun, an adjective, or a verb.

   • She earned two silver medals. [Noun]
   • She delivered a silver speech. [Adjective]
   • His worries have silvered his hair. [Verb]

   Computers may encounter difficulties determining whether a word is a noun, adjective, verb, or adverb in a sentence and interpreting the meaning of the sentence.

2. Sentence structure ambiguities

   For example, the “himself” in the sentence “John told Timothy to buy a new SUV for himself” could refer to John or Timothy. This ambiguity in sentence structure creates challenges for computers to fully understand the meaning of a sentence.

3. Lack of common sense

   Computers have difficulties understanding words such as “awesome” or “cool” answers to a question. This leads to computers encountering challenges understanding the mood that a person is in when he or she says “awesome!” or “cool!”.

As technology and more data is trained, computers will be able to overcome the challenges mentioned above and interpret text and sentences more accurately. The improved accuracy of text analytics will help marketers better understand their customers’ attitudes towards their brands or products or services, customer feedback, and other valuable information that businesses can use to better their marketing efforts and business operations.

If you have any questions about NLP, text analytics, or digital marketing, please feel free to reach out!