Intent Analysis is all about guesstimating the intention behind the information. The intention can be anything from wanting to buy, sell, complain, or the intention to cancel the purchase
Every intent behind an action or text has to be understood leading to many benefits for the company. Companies will be in a better position to understand the feedback of customers on their products and services. And what consumers feel about the competitor’s product. Intent Analysis is the new wave and evolution in NLP and AI that is all set to change how customer feedback is evaluated.
Intent analysis ups the game by assessing user intention behind any message segregating to identify if it is news, complaint or even a suggestion. The intent analyser classifier is of strategic value to this entire process. Amazon has been using intent analysis for the classification of messages into queries; bill related enquiries or even delivery based issues.
Types of Intent
Intent put is the intents or the intentions of the end-user conveyed by the user through bots. These intents can be segregated under two significant heads namely
- Casual intent – Also known as small talk intents and they are usually the openers and closer for conversations. A few examples of casual intents are hi, hello, good morning, ciao or bye. These intents usually trigger the bot to respond to the same with statements like, “Hello, what can I do for you?” or “Goodbye thanks for getting in touch with us”. These intents also contain affirmative intents like Ok, Yes please and negative intents like Nope and sorry. Having these intents will help the organisations to handle all sorts of intent rather than just taking the context out of the conversation.
- Business Intent – Supposing a customer is posting a query about the release date of a movie. It is this business intent that aids in finding out the release date of the film that is required. The business intent can help understand and summarise text, resulting in perfect resolution of the query intended.
Natural Language Processing is the algorithmic science of extracting the intention behind the text. The primary reason why the viewers see bot platforms growing is due to the initiation of numerous NLP as a service platforms.
NLP is a Deep Learning process that enables the computers and systems to derive meaning out of the input provided by the customers. When it comes to conversational AI software like Bot, it helps in assessing the intent of the input and then creates a response that is based on the context of the situation.
This behaviour is quite similar to that of a human operator who would have handled the query. In the case of bots that are known to carry out restricted functionalities, but developers can train them to perform other duties. While training the bots can be cumbersome, but once the results are in, it will be worth it.
Royal Bank of Scotland uses NLP in their conversational AI framework to enhance the customer experience. They do this by text analysis, surveys, complaints, emails and other forms of feedback they receive. This helps them identify the root of the issue and dissatisfaction.
How does NLP help to analyse the intent?
NLP’s are extensively using machine learning to use the input to take out the required information. Conversational AI like chatbots with NLP can go a long way in parsing multiple user intents to minimise the failures. The user inputs the message through chatbot, which is further fragmented to include a few words.
NLP then analyses the sentence in entirety and tries to understand the meaning through various factors like positioning, conjugation, plurality and many more factors proportional to the human speech. So it breaks down a huge bunch of information into smaller sentences. It takes our necessary information and correlates it with speech factors resulting in understanding the intent.
“The market worth of NLP by 2021 is expected to be $16.07 billion.”
Glass Dollar is one of the companies that link potential investors to founders. They are using the intent analysis technique to analyse the text that helps them find the best quality match. Within the sphere of understanding the linkage, they also use text analysis for classification of companies which saves them from manual labour.
It has also upped their predictions with an accuracy of 90%, which is incredible, considering the time and resources they would have spent sans the analysis. We can easily conclude that NLP is successful in analysing intent, which it does with the use of several techniques.
Let us explore five techniques that help NLP extract information and analyse its intent in the best possible manner.
- Named Entity recognition – This is the most basic but beneficial technique used for extracting the entities in the text. What it does is that it identifies fundamental concepts within the five and identifies people, locations and dates from the text. They are usually based on grammar rules and supervisor codes.
- Sentiment analysis – This is the most widely used technique. The technique is instrumental in cases such as customer surveys, reviews and also social media commenting. Here people pour out their mind about the product and services. The output of sentiment analysis is straightforward because it splits the analysis into 3 points namely: positive, negative and neutral. Complacency in response can be avoided after understanding the sentiment. A very recent study conducted by Mc Kinsey mentioned that after using such techniques companies overall were able to offer better services which led to an increase of 85% in the purchases made.
- Summarisation of text – This is the most important functionality of NLP for intent analysis. Companies are loaded with humongous data that needs to be sorted. If that is not done effectively, then the whole intention will go futile. The abstraction of text helps the company to summarise fresh text from heaps of data. The new text that comes gives the crux of the intended message which is good enough for further action. Text summarisation algorithms like LexRank, textrank and Latent Semantic Analysis helps in such summarisations.
- Aspect Mining – This technique identifies the different aspects of the text. Usually, this technique is used in sync with sentiment analysis which helps in complete extraction of matter from the text. Part speech tagging is the easiest methods of aspect mining.
- Topic Modeling – This is perhaps the most complicated form of identification of sense and intention in the information. This method identifies common words across the topics. This technique is especially useful for analysing open-ended survey responses.
NLP enabled chatbots, and other conversational AI software help enhance intent analysis business process. This will help companies elevate their version of customer experience and also increase overall growth and profitability. It is not enough to hear your customer; they also need to be understood.