The internet facilitates a never-ending creation of large volumes of unstructured textual data. Luckily, we have modern systems that can make sense of this kind of data.
Modern computer systems can make sense of natural languages using an underlying technology called NLP (natural language processing). This technology can process human language as input and perform one or more of the following operations:
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Significant implementations of NLP aren’t too far from us these days as most of our devices integrate AI (artificial intelligence), ML (machine learning) and NLP to enhance human-to-machine communications. Here are some common examples of NLP in action:
The use cases above show that AI, ML, and NLP are already being used heavily on the web. Since humans interact with websites using natural languages, we should build our websites with NLP capabilities.
This article will barely scratch the surface of NLP. This post will be useful for developers who already use NLP with Python but want to transition to achieve the same results with Node. Complete newbies will also learn a lot about NLP as a technology and its usage with Node.
To code along with this article, you will need to create an
index.js file and paste in the snippet you want to try then run the file with Node.
We can install natural by running the following command:
npm install natural
The source code to each of the following usage examples in the next section is available on Github. Feel free to clone it, fork it or submit an issue.
Let’s learn how to perform some basic but important NLP tasks using natural.
Tokenization is the process of demarcating and possibly classifying sections of a string of input characters. The resulting tokens are then passed on to some other form of processing. The process can be considered a sub-task of parsing input.
For example, in the text string:
The quick brown fox jumps over the lazy dog
The string isn’t implicitly segmented on spaces, as a natural language speaker would do. The raw input, the 43 characters, must be explicitly split into the 9 tokens with a given space delimiter (i.e., matching the string
" "or regular expression
Natural ships with a number of smart tokenizer algorithms that can break text into arrays of tokens. Here’s a code snippet showing the usage of the Word tokenizer:
// index.js var natural = require('natural'); var tokenizer = new natural.WordTokenizer(); console.log(tokenizer.tokenize("The quick brown fox jumps over the lazy dog"));
Running this with Node gives the following output:
[ 'The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog' ]
Stemming refers to the reduction of words to their word stem (also known as base or root form). For example, words such as cats, catlike, and catty will be stemmed down to the root word — cat.
Natural currently supports two stemming algorithms — Porter and Lancaster (Paice/Husk). Here’s a code snippet implementing stemming, using the Porter algorithm:
// index.js var natural = require('natural'); natural.PorterStemmer.attach(); console.log("I can see that we are going to be friends".tokenizeAndStem());
This example uses the
attach() method to patch
String as a shortcut to
tokenizeAndStem(). The result is the breaking down of the text into single words then an array of stemmed tokens will be returned:
[ 'go', 'friend' ]
Note: In the result above, stop words have been removed by the algorithm. Stop words are words that are filtered out before the processing of natural language(for example be, an, and to are all stop words).
Natural provides an implementation of four algorithms for calculating string distance, Hamming distance, Jaro-Winkler, Levenshtein distance, and Dice coefficient. Using these algorithms, we can tell if two strings match or not. For the sake of this project we will be using Hamming distance.
Hamming distance measures the distance between two strings of equal length by counting the number of different characters. The third parameter indicates whether the case should be ignored. By default, the algorithm is case sensitive.
Here’s a code snippet showing the usage of the Hemming algorithm for calculating string distance:
// index.js var natural = require('natural'); console.log(natural.HammingDistance("karolin", "kathrin", false)); console.log(natural.HammingDistance("karolin", "kerstin", false)); console.log(natural.HammingDistance("short string", "longer string", false));
3 3 -1
The first two comparisons return
3 because three letters differ. The last one returns
-1 because the lengths of the strings being compared are different.
Text classification also known as text tagging is the process of classifying text into organized groups. That is, if we have a new unknown statement, our processing system can decide which category it fits in the most based on its content.
Some of the most common use cases for automatic text classification include the following:
Natural currently supports two classifiers — Naive Bayes and logistic regression. The following examples use the
// index.js var natural = require('natural'); var classifier = new natural.BayesClassifier(); classifier.addDocument('i am long qqqq', 'buy'); classifier.addDocument('buy the q\'s', 'buy'); classifier.addDocument('short gold', 'sell'); classifier.addDocument('sell gold', 'sell'); classifier.train(); console.log(classifier.classify('i am short silver')); console.log(classifier.classify('i am long copper'));
In the code above, we trained the classifier on sample text. It will use reasonable defaults to tokenize and stem the text. Based on the sample text, the console will log the following output:
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.
Natural supports algorithms that can calculate the sentiment of each piece of text by summing the polarity of each word and normalizing it with the length of the sentence. If a negation occurs the result is made negative.
Here’s an example of its usage:
// index.js var natural = require('natural'); var Analyzer = natural.SentimentAnalyzer; var stemmer = natural.PorterStemmer; var analyzer = new Analyzer("English", stemmer, "afinn"); // getSentiment expects an array of strings console.log(analyzer.getSentiment(["I", "don't", "want", "to", "play", "with", "you"]));
The constructor has three parameters:
"pattern"are valid values
Running the code above gives the following output:
0.42857142857142855 // indicates a relatively negative statement
Using natural, we can compare two words that are spelled differently but sound similar using phonetic matching. Here’s an example using the
// index.js var natural = require('natural'); var metaphone = natural.Metaphone; var soundEx = natural.SoundEx; var wordA = 'phonetics'; var wordB = 'fonetix'; if (metaphone.compare(wordA, wordB)) console.log('They sound alike!'); // We can also obtain the raw phonetics of a word using process() console.log(metaphone.process('phonetics'));
We also obtained the raw phonetics of a word using
process(). We get the following output when we run the code above:
They sound alike! FNTKS
Users may make typographical errors when supplying input to a web application through a search bar or an input field. Natural has a probabilistic spellchecker that can suggest corrections for misspelled words using an array of tokens from a text corpus.
Let’s explore an example using an array of two words (also known as a corpus) for simplicity:
// index.js var natural = require('natural'); var corpus = ['something', 'soothing']; var spellcheck = new natural.Spellcheck(corpus); console.log(spellcheck.getCorrections('soemthing', 1)); console.log(spellcheck.getCorrections('soemthing', 2));
It suggests corrections (sorted by probability in descending order) that are up to a maximum edit distance away from the input word. A maximum distance of 1 will cover 80% to 95% of spelling mistakes. After a distance of 2, it becomes very slow.
We get the following output from running the code:
[ 'something' ] [ 'something', 'soothing' ]
Here’s a quick summary of what we’ve learned so far in this article:
The source code to each of the following usage examples in the next section is available on Github. Feel free to clone it, fork it or submit an issue.
Throughout the most recent couple of years, Node.js moved to backend development as well. Designers need to utilize a similar tech stack for the whole web project without learning another language for server-side development. Node.js is a device that adjusts JS usefulness and syntax to the backend.
Of late, organizations have been effectively changing from their backend tech stacks to Node.js. LinkedIn picked Node.js over Ruby on Rails since it took care of expanding responsibility better and decreased the quantity of servers by multiple times. PayPal and Netflix did something comparative, just they had a goal to change their design to microservices. We should investigate the motivations to pick Node.JS for web application development and when we are planning to hire node js developers.
Alongside prevalence, Node.js additionally acquired the fundamental JS benefits:
In addition, it’s a piece of a well known MEAN tech stack (the blend of MongoDB, Express.js, Angular, and Node.js — four tools that handle all vital parts of web application development).
In the event that you have JS designers in your group, it will be simpler for them to learn JS-based Node than a totally new dialect. What’s more, the front-end and back-end codebase will be basically the same, simple to peruse, and keep up — in light of the fact that they are both JS-based.
There’s another motivation behind why Node.js got famous so rapidly. The environment suits well the idea of microservice development (spilling stone monument usefulness into handfuls or many more modest administrations).
Microservices need to speak with one another rapidly — and Node.js is probably the quickest device in information handling. Among the fundamental Node.js benefits for programming development are its non-obstructing algorithms.
Node.js measures a few demands all at once without trusting that the first will be concluded. Many microservices can send messages to one another, and they will be gotten and addressed all the while.
Node.js was worked in view of adaptability — its name really says it. The environment permits numerous hubs to run all the while and speak with one another. Here’s the reason Node.js adaptability is better than other web backend development arrangements.
Node.js has a module that is liable for load adjusting for each running CPU center. This is one of numerous Node.js module benefits: you can run various hubs all at once, and the environment will naturally adjust the responsibility.
Node.js permits even apportioning: you can part your application into various situations. You show various forms of the application to different clients, in light of their age, interests, area, language, and so on. This builds personalization and diminishes responsibility. Hub accomplishes this with kid measures — tasks that rapidly speak with one another and share a similar root.
What’s more, Node’s non-hindering solicitation handling framework adds to fast, letting applications measure a great many solicitations.
Numerous designers consider nonconcurrent to be one of the two impediments and benefits of Node.js web application development. In Node, at whatever point the capacity is executed, the code consequently sends a callback. As the quantity of capacities develops, so does the number of callbacks — and you end up in a circumstance known as the callback damnation.
In any case, Node.js offers an exit plan. You can utilize systems that will plan capacities and sort through callbacks. Systems will associate comparable capacities consequently — so you can track down an essential component via search or in an envelope. At that point, there’s no compelling reason to look through callbacks.
So, these are some of the top benefits of Nodejs in web application development. This is how Nodejs is contributing a lot to the field of web application development.
I hope now you are totally aware of the whole process of how Nodejs is really important for your web project. If you are looking to hire a node js development company in India then I would suggest that you take a little consultancy too whenever you call.
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Node.js is a prominent tech trend in the space of web and mobile application development. It has been proven very efficient and useful for a variety of application development. Thus, all business owners are eager to leverage this technology for creating their applications.
Are you striving to develop an application using Node.js? But can’t decide which company to hire for NodeJS app development? Well! Don’t stress over it, as the following list of NodeJS app development companies is going to help you find the best partner.
Let’s take a glance at top NodeJS application development companies to hire developers in 2021 for developing a mind-blowing application solution.
Before enlisting companies, I would like to say that every company has a foundation on which they thrive. Their end goals, qualities, and excellence define their competence. Thus, I prepared this list by considering a number of aspects. While making this list, I have considered the following aspects:
I believe this list will help you out in choosing the best NodeJS service provider company. So, now let’s explore the top NodeJS developer companies to choose from in 2021.
JSGuru is a top-rated NodeJS app development company with an innovative team of dedicated NodeJS developers engaged in catering best-class UI/UX design, software products, and AWS professional services.
It is a team of one of the most talented developers to hire for all types of innovative solution development, including social media, dating, enterprise, and business-oriented solutions. The company has worked for years with a number of startups and launched a variety of products by collaborating with big-name corporations like T-systems.
If you want to hire NodeJS developers to secure an outstanding application, I would definitely suggest them. They serve in the area of eLearning, FinTech, eCommerce, Telecommunications, Mobile Device Management, and more.
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The NineHertz promises to develop a pro-active and easy solution for your enterprise. It has reached the heights in Node js web development and is considered as one of the top-notch Node js development company across the globe.
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