Defining Natural Language Processing
According to Investopedia, “Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables computers to analyze and understand human language, both written and spoken.” NLP was created for the purpose of building software that generates and comprehends natural languages so that users can have an easy, normal conversation with a computer rather than needing coding or programming knowledge with languages like Java or Python. According to Forbes, “technology is still evolving, but there are already many incredible ways natural language processing is used today.” This blog will discuss the evolution of natural language processing and how it works for survey platforms like ours.
Importance of Natural Language Processing
NLP is specifically used to understand the sentiment of your target audience’s written or recorded feedback. When you send out a survey, NLP works its magic to help computers read text, interpret speech clips, measure sentiment, and determine which parts are important. Voice and text are two common tools of communication we use to speak to each other. A common application of NLP would be in digital assistants, such as Siri, Alexa, and other applications which take spoken and written commands directly. When you ask Siri or Alexa to play music on your phone or call someone, natural language processing translates your command to the assistant into the actual query or action that the program needs to take.
Stages of NLP
There are five main stages of natural language processing – lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis. Without it getting too complicated, let’s learn more!
Lexical analysis deals with tackling language on a word-by-word basis. Lexical analysis is the process of attempting to understand the meaning of certain words, figure out the context, and the relationship of one word to the others. In this process, non-words, such as punctuation marks, are separated from the words, to avoid confusion. Lexical analysis is often considered an entry point for several data pipelines. It takes a source code file and breaks down the lines of code to a series of “tokens”, removing any whitespace or comments. This is known as the process of tokenization. After this step is complete, your computer will look up certain words in its dictionary and attempt to figure out their meanings, often breaking each word into its component parts of prefixes and suffixes to understand the morphemes individually and together.
Syntactic analysis, aka syntax analysis, is the process of ensuring that words in a particular sentence make sense. Natural language processing uses syntax analysis to make sure that the words in the sentence are logically placed. There are three main techniques that are used in syntax analysis – lemmatization, parsing, and word segmentation.
- Lemmatization: This is one of the most common processes in natural language processing. According to Medium, “Lemmatization is a technique which is used to reduce words to a normalized form.”
- Parsing: Parsing is the process of grammatically analyzing words in each
- Word Segmentation: Word segmentation is the process of separating the continuous text into individual words.
According to Medium, “semantic analysis means getting the meaning of a text.” Human communication, from the perspective of computers, is a complicated puzzle waiting to be solved, one word or passage at a time. Human beings understand text using context clues, such as the tone of certain words in a passage. Semantic analysis accomplishes this step by breaking down the emotions and extracting valuable data from the message. For surveys, this can help determine the sentiment behind the feedback from your customers and employees, to gauge how well they trust your business and how you can retain them.
After going word-by-word and then analyzing phrases and the tone behind them, discourse integration uses a previous phrase to analyze the current one. When it comes to human communication, a passage usually has sentences which influence how future ones should be interpreted. Either with how pronouns work as substitutes for subjects, or how following sentences build on the subject previously discussed in a passage. Discourse integration takes the individual phrases broken down in the previous stages of NLP and finds the relationships between them.
Now that the sentences have been assessed in connection to each other with discourse integration, pragmatics looks at how a given sentence can be applied in the real-world. Pragmatics goes beyond the specific text in the passage being analyzed, to understand the societal meaning of the sentences in the passage. By applying knowledge of concepts and ideas from the outside world, pragmatics helps computers put a certain passage in the context of the knowledge that humans would have, but computers may not have had before the evolution of NLP technology.
Applications of NLP
Email filters are known to be one of the most common and basic forms of natural language processing. In all email platforms, the most common filter is the spam filter. Such filters scan through the language of the email, looking for common keywords, phrases, and suspicious addresses which would make these emails more likely to be malicious or superfluous to email users. Let’s look at Gmail as an example. Gmail categorizes a user’s inboxinto three main categories – primary, social, and promotions. This allows the email user to manage their inbox wisely and create more filters to deal with more important emails.
Nowadays, any phone you purchase comes with a smart assistant such as Google, Siri, and Alexa. Thanks to voice recognition, such amazing smart assistants are familiar with text-to-speech patterns. Over time, we have gotten comfortable with simply asking these assistants to perform tasks that we would usually do ourselves, including grocery shopping, calling friends and family, connecting other devices to a TV or phone, and playing music. Nowadays, we expect these smart assistants to be our daily calendars. The more the assistants learn our voice and get familiarized with us, the more personal our interactions would be with them.
How Does NLP Work in PxidaX?
When it comes to understanding customer and employee feedback, nobody does it like PxidaX. Using our proprietary cognitive and behavioral analytics, our visualizations are able to digest written and recorded feedback from your survey audience, to help you get the most from the data they provide you through our surveys. Our What Matters Most question and survey template is powered by AI and ML algorithms, including NLP programming, so that written feedback can be parsed by sentiment, highlighted by importance, and grouped by frequency across survey audiences. By using PxidaX for launching easy-to-build surveys and understanding your survey responses, your team can get to the heart of the survey data much easier. Try PxidaX for a free trial today!