Decoding persuasion: a survey on ML and NLP methods for the study of online persuasion

5 Natural Language Processing Libraries To Use

Natural language processing (NLP) is important because it enables machines to understand, interpret and generate human language, which is the primary means of communication between people. By using NLP, machines can analyze and make sense of large amounts of unstructured textual data, improving their ability to assist humans in various tasks, such as customer service, content creation and decision-making.

Additionally, NLP can help bridge language barriers, improve accessibility for individuals with disabilities, and support research in various fields, such as linguistics, psychology and social sciences.

Here are five NLP libraries that can be used for various purposes, as discussed below.

NLTK (Natural Language Toolkit)

One of the most widely used programming languages for NLP is Python, which has a rich ecosystem of libraries and tools for NLP, including the NLTK. Python's popularity in the data science and machine learning communities, combined with the ease of use and extensive documentation of NLTK, has made it a go-to choice for many NLP projects.

NLTK is a widely used NLP library in Python. It offers NLP machine-learning capabilities for tokenization, stemming, tagging and parsing. NLTK is great for beginners and is used in many academic courses on NLP.

Tokenization is the process of dividing a text into more manageable pieces, like specific words, phrases or sentences. Tokenization aims to give the text a structure that makes programmatic analysis and manipulation easier. A frequent pre-processing step in NLP applications, such as text categorization or sentiment analysis, is tokenization.

Words are derived from their base or root form through the process of stemming. For instance, "run" is the root of the terms "running," "runner," and "run." Tagging involves identifying each word's part of speech (POS) within a document, such as a noun, verb, adjective, etc.. In many NLP applications, such as text analysis or machine translation, where knowing the grammatical structure of a phrase is critical, POS tagging is a crucial step.

Parsing is the process of analyzing the grammatical structure of a sentence to identify the relationships between the words. Parsing involves breaking down a sentence into constituent parts, such as subject, object, verb, etc. Parsing is a crucial step in many NLP tasks, such as machine translation or text-to-speech conversion, where understanding the syntax of a sentence is important.

Related: How to improve your coding skills using ChatGPT?

SpaCy

SpaCy is a fast and efficient NLP library for Python. It is designed to be easy to use and provides tools for entity recognition, part-of-speech tagging, dependency parsing and more. SpaCy is widely used in the industry for its speed and accuracy.

Dependency parsing is a natural language processing technique that examines the grammatical structure of a phrase by determining the relationships between words in terms of their syntactic and semantic dependencies, and then building a parse tree that captures these relationships.

Stanford CoreNLP

Stanford CoreNLP is a Java-based NLP library that provides tools for a variety of NLP tasks, such as sentiment analysis, named entity recognition, dependency parsing and more. It is known for its accuracy and is used by many organizations.

Sentiment analysis is the process of analyzing and determining the subjective tone or attitude of a text, while named entity recognition is the process of identifying and extracting named entities, such as names, locations and organizations, from a text.

Gensim

Gensim is an open-source library for topic modeling, document similarity analysis and other NLP tasks. It provides tools for algorithms such as latent dirichlet allocation (LDA) and word2vec for generating word embeddings.

LDA is a probabilistic model used for topic modeling, where it identifies the underlying topics in a set of documents. Word2vec is a neural network-based model that learns to map words to vectors, enabling semantic analysis and similarity comparisons between words.

TensorFlow

TensorFlow is a popular machine-learning library that can also be used for NLP tasks. It provides tools for building neural networks for tasks such as text classification, sentiment analysis and machine translation. TensorFlow is widely used in industry and has a large support community.

Classifying text into predetermined groups or classes is known as text classification. Sentiment analysis examines a text's subjective tone to ascertain the author's attitude or feelings. Machines translate text from one language into another. While all use natural language processing techniques, their objectives are distinct.

Can NLP libraries and blockchain be used together?

NLP libraries and blockchain are two distinct technologies, but they can be used together in various ways. For instance, text-based content on blockchain platforms, such as smart contracts and transaction records, can be analyzed and understood using NLP approaches.

NLP can also be applied to creating natural language interfaces for blockchain applications, allowing users to communicate with the system using everyday language. The integrity and privacy of user data can be guaranteed by using blockchain to protect and validate NLP-based apps, such as chatbots or sentiment analysis tools.

Related: Data protection in AI chatting: Does ChatGPT comply with GDPR standards?

Four Natural Language Processing Techniques To Increase Your Understanding

Technology grows at an exponential rate.

Think back to what life was like before we had the internet (if you're old enough to). What would the you from 1990 think of that smartphone you're using today to stream TV shows and video chat with relatives from across the country?

Technology changes and grows so fast that a lot of it happens without us even noticing. Natural language processing is a great example of one of these trends -- something that is happening around us all the time that we don't even realize. Some simple, everyday examples of natural language processing tools are spellcheck, autocorrect, spam filters and the Google search bar that tries to predict what you are going to say. These things have made our lives easier and more efficient.

But natural language processing, or NLP, is used in so many different ways, across a huge array of industries. Researchers are breaking ground with NLP innovations and imagining a future that will keep us right on track with that exponential growth.

Keep reading to learn about four natural language processing techniques that are changing people's lives.

First, A Little Bit Of Background

Natural language processing is an interdisciplinary field that includes both computer science and linguistics. As humans, we have an innate ability to understand other people who speak the same language. Babies respond differently to human language than they do to other sounds. It's just something that's programmed into us.

If someone has a different accent -- say they're from New Jersey or South Carolina -- we might notice that they pronounce certain words a little differently, but we would still have no problem understanding them.

This is a much more difficult task for computers to do because they're not naturally geared toward understanding language the way that we are. Anything and everything they know about language has to be taught to them through programming. And the degree of complexity we've been able to program them with is getting more advanced every day.

Let's take Siri as an example. You can ask her about the weather, tell her to call your mom or put on your favorite playlist. She can't do everything, but she sure can do a lot.

The first speech recognition system was developed by Bell Labs in 1952. Her name was Audrey, and her main ability was that she could recognize the numbers one through 10 when spoken, slowly. That was it.

So there have been decades of study devoted to getting natural language processing techniques to where they are today. Here are some examples.

1. Livox

Livox was an app envisioned and created by Carlos Pereira, a Brazilian father in search of a better life for his daughter. Clara was born with cerebral palsy and was unable to walk or speak. Carlos tried every type of treatment he could find, but nothing seemed to help her, so he decided to take matters into his own hands and create Livox.

The app is essentially a communication device for people with different types of disabilities.

It uses natural language processing to be able to recognize and assist people in their communication. It's customizable to work with a wide range of different needs and affordable so that anyone can have access to it.

It works in more than 25 languages, and Pereira started a nonprofit called Inclusion Without Borders to try to make the app available to free for those in need. Livox was awarded a UN World Summit Award, and Google gave Inclusion Without Borders a $550,000 grant in 2016.

2. Google Translate

If you haven't had a reason to test out Google's translation feature, now is the time. Copy and paste this sentence into Google Translate: Die Grenzen meiner Sprache bedeuten die Grenzen meiner Welt.

Google's natural language processing technology will be able to automatically recognize that this sentence is in German, and then it will give you the English translation, which is: "The limits of my language are the limits of my world." Austrian philosopher Ludwig Wittgenstein is known for that quote, but you could say that it applies to all us.

Google Translate gives us access to more than 100 languages spoken throughout the world, and it is used by 500 million people every single day. And Google is just one of many tech companies out there that is using natural language processing to help with translations. In fact, there is even a Swedish startup that claims that by 2021 it will be able to translate the language of dolphins. No joke.

3. SignAll

SignAll is a company out of Budapest, Hungary, that is changing the way that the deaf community is able to communicate.

Founded in 2016, it's a company that uses natural language processing to turn sign language into text. Web cameras capture the physical movements of sign language. The company's specialized systems are then programmed to recognize and organize the input and turn it into words and sentences.

So if you have a friend who is deaf, but you don't know any sign language, SignAll's technology will allow you to be able to translate in real time what they're saying.

4. 98point6

98point6 is a Seattle-based startup that is looking to completely shake up the way we do health care.

This bold tech team is using natural language processing to allow patients to consult with doctors digitally instead of having to wait in line surrounded by other sick people. Have a bad rash and don't know what it is? You can consult with a doctor from the comfort of your oatmeal bath.

A chatbot walks users through some initial questions and then turns them over to an on-call physician, who can diagnose illnesses and prescribe treatments.

These NLP Techniques Are Just The Tip Of The Iceberg

Every day there are tech companies using NLP techniques in exciting and innovative ways. You'll want to keep NLP in your sights and think about the ways it might be useful to your business or career.

Natural Language Processing (NLP): What It Means, How It Works

What Is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables computers to analyze and understand human language, both written and spoken. It was formulated to build software that generates and comprehends natural languages so that a user can have natural conversations with a computer instead of through programming or artificial languages like Java or C. Key Takeaways Natural language processing (NLP) employs computer algorithms and artificial intelligence to enable computers to recognize and respond to human communication. While several NLP methods exist, they typically involve breaking speech or text into discrete sub-units and then comparing these to a database of how these units fit together based on past experience. Text-to-speech apps, which are now found on most iOS and Android platforms, along with smart speakers like the Amazon Echo (Alexa) or Google Home, have become ubiquitous examples of NLP over the past few years. Understanding Natural Language Processing (NLP) Natural Language Processing (NLP) is one step in a larger mission for the technology sector—namely, to use artificial intelligence (AI) to simplify the way the world works. The digital world has proved to be a game-changer for a lot of companies as an increasingly technology-savvy population finds new ways of interacting online with each other and with companies. Social media has redefined the meaning of community; cryptocurrency has changed the digital payment norm; e-commerce has created a new meaning of the word convenience, and cloud storage has introduced another level of data retention to the masses. Through AI, fields like machine learning and deep learning are opening eyes to a world of all possibilities. Machine learning is increasingly being used in data analytics to make sense of big data. It is also used to program chatbots to simulate human conversations with customers. However, these forward applications of machine learning wouldn't be possible without the improvisation of Natural Language Processing (NLP). Stages of Natural Language Processing (NLP) NLP combines AI with computational linguistics and computer science to process human or natural languages and speech. The process can be broken down into three parts. The first task of NLP is to understand the natural language received by the computer. The computer uses a built-in statistical model to perform a speech recognition routine that converts the natural language to a programming language. It does this by breaking down a recent speech it hears into tiny units, and then compares these units to previous units from a previous speech. The output or result in text format statistically determines the words and sentences that were most likely said. This first task is called the speech-to-text process. The next task is called the part-of-speech (POS) tagging or word-category disambiguation. This process elementarily identifies words in their grammatical forms as nouns, verbs, adjectives, past tense, etc. Using a set of lexicon rules coded into the computer. After these two processes, the computer probably now understands the meaning of the speech that was made. The third step taken by an NLP is text-to-speech conversion. At this stage, the computer programming language is converted into an audible or textual format for the user. A financial news chatbot, for example, that is asked a question like "How is Google doing today?" will most likely scan online finance sites for Google stock, and may decide to select only information like price and volume as its reply. Special Considerations NLP attempts to make computers intelligent by making humans believe they are interacting with another human. The Turing test, proposed by Alan Turing in 1950, states that a computer can be fully intelligent if it can think and make a conversation like a human without the human knowing that they are actually conversing with a machine. One computer in 2014 did convincingly pass the test—a chatbot with the persona of a 13-year-old boy. This is not to say that an intelligent machine is impossible to build, but it does outline the difficulties inherent in making a computer think or converse like a human. Since words can be used in different contexts, and machines don't have the real-life experience that humans have for conveying and describing entities in words, it may take a little while longer before the world can completely do away with computer programming language.

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