Natural Language Processing (NLP) is a field of study that articulates the connection between computers and humans in natural spoken language. We know that computers rely on binary number systems with 0’s and 1’s to communicate data and process information. Computer and App developers write programs in the machine language and build a human-friendly interface for regular users to understand the features and functions of the software and utilize it to their convenience. NLP allows computers directly to process human text and voice and process it to give appropriate feedback.
Natural Language Processing (NLP) is the amalgamation of computer science, Artificial Intelligence(AI), and linguistics. The primary purpose of NLP is to make a machine understand our language processing and come back with appropriate human-like responses. A great example would be AI Digital assistants like Alexa, Siri, or any chatbots. Especially with voice recognition and processing, these AI assistants utilize NLP and apply reinforced machine learning to come forward with appropriate search responses for our queries.
Ultimately, NLP allows computers to perform sentiment analysis, determine which parts of human language are significant, and understand the underlying message properly. NLP is challenging due to the nature of complicated sentence structure, grammar formats, and exceptional rules followed by every language, leading to the large amount of unstructured data the machine has to process.
NLP can be categorized based upon the nature of human data and techniques utilized to process the language.
1. Optical Character Recognition (OCR)
OCR is a robust data capture solutions technology that recognizes handwritten text content and derives the information. With digitization in full bloom, all the handwritten text, either electronic or paper documents, are being migrated into the server database for better management analysis. OCR allows this task to be composed without any manual data entry work involved.
NLP enables the systems to recognize relevant concepts in the document to gather information beneficial to create a structured database for machine learning analytics.
For example, the words, numbers, and tables could be identified separately, and nouns like addresses and names can be further scrutinized to create a complete customer data set easily.
2. Text Summarization
Text summarization NLP is used by experts to assess information and trends present in research articles and journals.
Text Summarization uses extraction and abstraction techniques to achieve the results. The extraction process assesses large amounts of textual data and gathers precise and informative summaries with essential keywords, and the Abstraction tools create new summaries based on the assessment of the source text.
3. Sentiment Analysis
As the word suggests, Sentiment Analysis is the process of mining emotions from the given text. Here the NLP analyzes various unstructured data from multiple social media platforms and content assets like Facebook, Instagram, blogs, customer reviews forums, online surveys and tries to identify and extract opinions from the given text. In sentiment analysis, the data is first preprocessed to remove unnecessary prepositions and symbols. The main focus is on the adjective and the corresponding keywords; for example, if you take data from a customer review page, you can look for the right message.
Customer service – quick to respond
Packaging-Compact and secure
The features extracted are used to calculate the repetition index and conclude a polarity of sentiments if the customer feels positive or negative about the product. The NLP provides insights into whether most customers found the product pricey or which percentage of users were satisfied by the product and could use this information in future considerations while designing a new product.
4. Machine Translation
Machine Translation is a vital NLP tool that automatically converts one natural language into another, preserving the meaning of the input text and analyzing and generating the text in the output language.
Google translate is an excellent example of Machine Translation. It uses statistical models by investigating vast volumes of data in both languages, finding the compatibility between the word from the source language, and translating it into an appropriate word or sentence from the objective language to ensure it follows the linguistic structure.
5. Speech Recognition
Speech recognition and processing using NLP allow devices, such as smartphones and home assistants, interact with users with their verbal language.
AI systems like Alexa or Siri get voice input in natural language. After receiving the signal and processing it to eliminate the noise, the cleaned file is converted into Artificial language like speech recognition.NLP systems have a lexicon and a set of grammar rules coded into the system for the particular language. It utilizes the algorithms to perform statistical machine learning and apply these rules to the text and determine the most likely meaning behind the question asked by the person. This way, we can have an intelligent conversation with our digital assistants.
We already mentioned that everything we express, either verbally or in writing, carries enormous amounts of information. As humans, we are intelligent and understand the words, sentences, and structure based on the tone and context.
But say, you are looking to scale and analyze several millions of people’s conversations and text declarations. In that case, the data also gets very big, so NLP actively relies on Machine Learning (ML) to process large databases.
Machine learning methods utilize algorithms and weight optimization to make the best final prediction. On the other hand, deep learning relies on artificial neural networks(ANN) with many layers to process the data.
Deep learning provides NLP processes with more speed and agility to automatically learn good features or representations from raw inputs. Deep learning offers the machine to apply modeling approaches like sequence-to-sequence prediction and read the features of natural language independently with the ANN, so external extraction by data scientists is not required. The large end-to-end deep learning models easily fit the natural language problems with better regularisation and optimization methods.
The Artificial Intelligence(AI) market size worldwide was valued at USD 62.35 billion in 2020 and is bound to expand with a compound annual growth rate. Many tech companies and businesses are already invested in AI, and other sectors like healthcare are investing in R&D to use patient details to make intelligent diagnoses or book appointments automatically based upon the patient’s history using ML and AI.
The industry leader of streaming services, Netflix relies on AI and ML technology. We all know that Netflix revolutionized the streaming and refined movie and entertainment experience to the next level.
Despite all the fantastic services, Netflix faces tough competition for viewers from Amazon prime, Disney, and similar streaming sites. To remain on top of the time, Netflix mines user data using ML to serve customers and recommend movies or series that would appeal to them. To expand users worldwide and reach farther, they are compelled to support entertainment in other languages apart from English and provide them with quality subtitles in the regional languages. This is where the use of AI and NLP comes into play.
Netflix uses a novel model to produce accurate machine learning results with two steps.
Transcribing: The audio is converted into English words and collected as a database using Nvidia GPUs to crunch through the data.
Translation: Machine translation is applied from English to other languages where Netflix wants to distribute and provide subtitles.
They initially simplify the sentence using a corpus of words and phrases and translate that instead. Netflix’s model is called the automatic preprocessing model (APP); this framework is applied to all English-language sources. The simplified text is sent to machine translation to create subtitles in specific languages.
This way, they are able to automate the process of subtitles, which would typically take months to translate into multiple languages. And the ML applied to customer data to provide them with customized and dedicated recommendations to multiply the watch time by manifold.
Nordstrom is an outstanding fashion retailer offering a high range of clothing, shoes, and accessories for men, women, and kids. They are known for their hospitality and customer service, guiding the users with the help of a stylist and salesperson to ensure a comfortable and personalized shopping experience.
And now, due to the global pandemic restriction, all businesses shut down all the offline stores. And many of them resorted to e-commerce platforms. But, Nordstorm did not want to compromise on their customer service and wanted to make their online store personalized and comfortable as their offline shopping experience.
Nordstrom revamped its data infrastructure to enhance the customer experience and sought to create an AI-supported Nordstrom Analytical Platform (NAP) that employs real-time data collection, event streaming–centric analytics, and collects insights on everything from customers services to credit. They have also modified the platform to elevate your offline shopping experience without human interactions. They have a feature that notifies you about a particular product in your mobile cart as soon as you walk into a store; the app can track your location and pinpoint where your desired product is located within the store, allowing you to navigate without any external assistance.
With AI models leveraged at Nordstrom, users benefit from the timely delivery of information like tracking their packages. And this has redefined their online shopping experience with better selection, personalized dynamic looks, sophisticated style boards, and recommended choices.
With the growth in e-commerce and digital marketing, knowing what customers are saying on social media is very crucial to provide an appealing product or service and authentic customer experience. A study shows that about 90% of people tend to buy from brands they follow on social media. Therefore, providing a two-way customer experience is essential, and NLP elevates the customer data monitoring and response to feedback. Social Media Marketing Services along with Online Reputation Management from Vajra helps any industry build and accelerate the social dialogue and gain momentum in social platforms. More traffic to your application and social media means an extensive database to work and test NLP and AI abilities, so the digital assistants or chatbots can be more robust and sound closer to a human.