Natural Language Processing

Natural Language Processing (NLP) is at work all around us, making our lives easier at every turn, yet we don’t often think about it. From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging.

NLP is a field of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages.

When we communicate (either written or verbally) carries huge amounts of information. The topic we choose, our tone, our choice of words, everything adds some type of information that can be interpreted and value extracted from it. But there is a problem: one person may generate hundreds or thousands of words in a declaration, each sentence with its corresponding complexity.  Data generated from conversations, declarations or even tweets are examples of unstructured data. Unstructured data doesn’t fit neatly into the traditional row and column structure of relational databases, and represent the vast majority of data available in the actual world. It is messy and hard to manipulate. In the present digitized world 80% of the data generated is unstructured ( As unstructured data grows, NLP technology continues to evolve to better understand the nuances, context, and ambiguities of human language. Curious to know what’s ahead for natural language processing? Let’s explore five NLP trends for 2022.

1. Transfer Learning

Transfer Learning uses the knowledge that a model has gained from being trained on one task to solve another task.  So, instead of building and training a model to start with, which is expensive, time-consuming, and could involve huge amounts of data, you just fine-tune a pre-trained model. This means that businesses can complete NLP tasks faster using smaller amounts of labeled data

2. Fake News and Cyberbullying Detection

The amount of fake news and hateful or abusive phrases in user-generated content (UGC) has sharply increased over the last decade with ever-growing political discrepancies and populists fueling the trend even more. Using examples from the 2016 US election, H. Allcott, and M. Gentzkow, in their article Social Media and Fake News in the 2016 Election, suggested that ‘one fake news article was about as persuasive as one TV campaign ad’ and has the potential to impact close political battles. However NLP has become an essential tool to reduce the time and human effort to detect and prevent the spread of fake news and misinformation. Recently, machine learning transformer based models BERT and ALBERT have demonstrated the use and effectiveness in retrieving and classifying fake news in a highly specialized domain of COVID-19 (

Another way NLP is being used for positive impact is cyberbullying detection. Classifiers are being built to detect the use of offensive, and insulting language, or hate speech across social media.

3. Monitoring Social Media using NLP

Social media analytics leverages the ability to gather and find meaning in data gathered from social channels to support business decisions — and measure the performance of actions based on those decisions through social media  In 2019, there were 3.4 billion active social media users in the world ( On YouTube alone, one billion hours of video content are watched daily ( Every indicator suggests that we will see more data produced over time, not less. Social media analytics tools such as behavior analysis, sentiment analysis and clustering analysis can measure tone and intent, understand the concerns of social media participants, and uncover hidden conversations and unexpected insights. These tools help companies gauge brand sentiment, identify opportunities for improvement, detect negative comments on the fly (and respond proactively), and gain a competitive advantage

4. The use of Multilingual NLP will increase

Most NLP advances to date have been focused on English. But companies like Google and Facebook are now publishing pre-trained multilingual models, which perform just as well or better than monolingual models. Given the increasing smartphone and Internet penetration in developing countries where local, or low-resource, languages are usually spoken, building more NLP models in these languages will become ever more important. Before 2019 multilingual models were unheard of, then Facebook introduced XLM-R and more recently M2M-100, the first multilingual machine translation model that can translate 100 languages without relying on English data ( Open-source libraries are also following in the footsteps of Google and Facebook, so we can expect to see a growing trend in multilingual NLP models this year.

5. Using a mix of Supervised & Unsupervised Machine Learning Techniques

When training a model for NLP, combining both supervised and unsupervised methods seems to provide more accurate results.

Supervised learning, commonly used for tasks such as topic classification, requires a large amount of tagged data and many iterations until a model can make accurate predictions. In unsupervised learning, on the other hand, there’s no labeled data: the model learns from input data and is able to detect patterns and make inferences on unseen data, on its own. An example of this is clustering, where similar objects are grouped together. A combination of supervised and unsupervised machine learning is called Semi-Supervised Machine Learning. These problems sit in between both supervised and unsupervised learning.

Combining supervised and unsupervised recently used for phenotyping complex diseases with its application to obstructive sleep apnea to sentiment polarity detection (

The global natural language processing (NLP) market reached a value of US$ 14.27 billion in 2021 ( Looking forward, the market is projected to reach a value of US$ 61.03 billion by 2027 exhibiting a CAGR of 26.60% during 2022-2027 ( In the years to come, NLP will become accessible thanks to ready-to-use pre-trained models and low-code, no-code tools that are avaible to everyone. Businesses, in particular, will continue to benefit from NLP, from improving their operations and customer satisfaction to reducing costs and making better decisions.

About Victor Allen

Our blog writer, Victor Allen, is a data scientist with over 20 years of subject matter expertise and robust knowledge in information technology, data science, machine learning, big data analytics, cyber security incident response and cyber security intelligence training. He provides advanced capabilities in Data Science methodologies and techniques of Data Extraction, Data Mining, Data Wrangling, Feature Extraction, Statistical Modeling, Predictive Modeling, and Data Visualization. Before joining Collabraspace in 2021, Mr. Allen worked as a data science technical lead developing and teaching foundational data science curriculum for the National Cryptologic Institute.