Getting Richer Insights using Natural Language Processing and Machine Learning

Getting Richer Insights using Natural Language Processing and Machine Learning

To grasp the meaning of text documents, machine learning (ML) for natural language processing (NLP) and text analytics uses machine learning algorithms and “narrow” artificial intelligence (AI). Social media comments, online reviews, survey responses, and even financial, medical, legal, and regulatory paperwork are all examples of documents that incorporate text. In natural language processing and text analytics, the role of machine learning and AI is to improve, accelerate, and automate the underlying text analytics functions and NLP features that convert unstructured text into usable data and insights.

Machine Learning for Natural Language Processing

Let's go over some fundamental concepts before diving into how to use machine learning and AI for NLP and text analytics.

Above all, “machine learning” actually means “machine teaching.” We know what the machine needs to learn, therefore our job is to build a learning framework and offer the machine with properly formatted, relevant, and clean data to work with.

We're talking about a mathematical representation when we say "model." Input is crucial. A machine learning model is made up of all of the knowledge it has gained from its training data. As additional knowledge is gained, the model evolves.

A machine learning model, unlike algorithmic programming, can generalise and cope with novel scenarios. If a situation looks similar to something the model has seen before, it can utilise its previous "learning" to evaluate it. The idea is to develop a system in which the model improves over time at the task you've given it.

A set of statistical algorithms for detecting portions of speech, entities, sentiment, and other features of text are used in machine learning for NLP and text analytics. The strategies can be expressed as a model that is then applied to different texts, which is referred to as supervised machine learning. It could also be a set of algorithms that extract meaning from vast volumes of data, known as unsupervised machine learning. It's critical to comprehend the differences between supervised and unsupervised learning, as well as how to combine the two into a single system.

Machine learning for text data necessitates a unique technique. This is due to the fact that text data can have hundreds of thousands of dimensions (words and phrases), but it is usually sparse. The English language, for example, has roughly 100,000 terms in common use. However, there are only a few dozen of them in each given tweet. This is different from video content, which has a high dimensionality but a lot of data to work with, so it's not as sparse.

Supervised Machine Learning for Natural Language Processing and Text Analytics

A batch of text documents is tagged or annotated with samples of what the machine should search for and how it should interpret that element in supervised machine learning. These papers are used to "train" a statistical model, which is then given untagged text to investigate.

As the model learns more about the texts it examines, you can retrain it with larger or better datasets. For example, supervised learning can be used to train a model to analyse movie reviews and then subsequently to factor in the reviewer's star rating.

The most popular supervised NLP machine learning algorithms are:

·         Support Vector Machines

·         Bayesian Networks

·         Maximum Entropy

·         Conditional Random Field

·         Neural Networks/Deep Learning

All you really need to know if come across these terms is that they represent a set of data scientist guided machine learning algorithms.

Sentiment Analysis

The act of detecting if a piece of writing is positive, negative, or neutral, and then assigning a weighted sentiment score to each entity, subject, topic, and category within the document is known as sentiment analysis. This is a really difficult chore that changes greatly depending on the situation. Take the phrase "sick burn," for example. This might really be a favourable statement in the context of video games.

It would be difficult to create a set of NLP rules that would account for every potential sentiment score for every possible phrase in every possible circumstance. However, by using pre-scored data to train a machine learning model, it may learn to distinguish between what "sick burn" means in the domain of video games and what it means in the context of healthcare. Each language, predictably, requires its own sentiment categorization model.

Background: What is Natural Language Processing?

The study and development of computer systems that can read voice and text as humans naturally speak and type it is referred to as natural language processing. We all use colloquialisms, abbreviations, and don't always care to correct misspellings, so human communication can be frustrating at times. Because of these inconsistencies, computer analysis of natural language is at best problematic. However, both NLP approaches and machine learning algorithms have advanced dramatically in the recent decade.

Any piece of literature can be divided into three categories:

Semantic Information

The specific meaning of a word is known as semantic information. Depending on the definition of bat: winged mammal, wooden stick, or something else entirely, a sentence like "the bat flew through the air" might have many meanings. Understanding the meaning of a sentence requires knowing the applicable definition.

“Billy hit the ball over the house,” for example. You might assume that the ball in question is a baseball as a reader, but how can you know? A volleyball, tennis ball, or even a bocce ball might be used. We assume baseball because that is the most common form of ball that is "hit" in this manner, but without natural language machine learning, a computer would have no idea what to do.

Syntax Information

The sentence or phrase structure, often known as syntactic information, is the second important component of text. “Sarah joined the group already having some search experience,” for example. Who has the most search experience in this group? Is it Sarah or the rest of the group? The line has several meanings depending on how you read it in terms of Sarah's ability.

Context Information

Finally, you must be aware of the context in which a word, phrase, or sentence is used. What is the concept that is being debated? Are they talking about healthcare or video games when they claim something is "sick"? When used in the context of gaming, the word "sick" has a positive connotation, whereas it almost always has a negative connotation when used in the context of healthcare.

Language is a maze of problems and complexities. Meaning differs from one speaker to the next and from one listener to the next. When it comes to interpreting text data, machine learning can be a useful tool. In fact, it's critical Because text analytics based solely on rules is a dead end. However, using just one type of machine learning model isn't adequate. Machine learning has a lot of subjective features. To fit your perspective, you must tune or train your system.