Natural language processing, also known as NLP, is a technique used by software to understand meanings, sentiments, attitudes, and other insights of 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 Jonathan’s mood and sentiment using the sentence.
To analyze text, the computer would use lexical analysis or part-of-speech tagging to determine is a noun, verb, adjective, or adverb.
Then, the computer can determine the structure of the sentence. For example, the computer needs to figure out that Jonathan is taking an action (watched) on a TV series (a season of The Walking Dead). More, the computer can perform a semantic analysis to gain more insights 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 very 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 on their customers’ feedback, sentiment, and other 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 determine the relationships in a sentence.
3. Lack of common sense. Computers may have difficulties understanding colloquial words such as “awesome” or “cool”. This difficulty creates challenges for to understand a person’s mood or the meaning of a sentence.
As technology advances 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 through social media posts, comments, reviews, and other online content. These insights will help businesses increase the efficiency of their marketing efforts and business operations.
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