Text Classification Using Machine Learning and Natural Language Processing

Text can be a very rich source of information, but due to its unstructured nature, extracting insights from it can be difficult and time-consuming.

Sorting text data is becoming easier because to developments in natural language processing and machine learning, both of which lie under the broad umbrella of artificial intelligence.

It works by swiftly and cost-effectively analysing and structuring text so that businesses may automate processes and uncover insights that lead to better decision-making.

Learn more about text classification, how it works, and how simple it is to get started using no-code text classification tools like MonkeyLearn's sentiment analyzer by reading on.

What is Text Classification, and how does it work?

Text classification is a machine learning technique for categorising open-ended text into a collection of predetermined categories. Text classifiers can organise, arrange, and categorise almost any type of text, including documents, medical research, and files, as well as text found on the internet.

New articles, for example, can be categorised by themes; support tickets can be prioritised; chat dialogues can be categorised by language; brand mentions can be categorised by sentiment; and so on.

Text classification is a basic problem in natural language processing that has a wide range of applications, including sentiment analysis, topic labelling, spam detection, and intent identification.

Here's how it works in practise:

"The user interface is quite simple and straightforward."

This sentence can be fed into a text classifier, which will analyse the content and apply relevant tags like UI and Easy To Use.

What is the significance of text classification?

Unstructured data accounts for over 80% of all data, with text being one of the most common categories. Because analysing, comprehending, organising, and sifting through text data is difficult and time-consuming due to its messy nature, most businesses do not exploit it to its full potential.

This is where machine learning and text classification come into play. Companies may use text classifiers to quickly and cost-effectively arrange all types of relevant content, including emails, legal documents, social media, chatbots, surveys, and more. Companies can save time analysing text data, automate business processes, and make data-driven business choices as a result of this technology.

What are the benefits of using machine learning for text classification? Some of the more compelling reasons include:

Scalability

Manually evaluating and arranging data is time-consuming and inaccurate. Machine learning can evaluate millions of surveys, comments, emails, and other documents at a fraction of the cost, and in as little as a few minutes. Text classification software may be scaled to meet the needs of any business, big or small.

Analyses in real time

Companies must recognise dangerous conditions as soon as feasible and take immediate action (e.g., PR crises on social media). Machine learning text classification can track your brand mentions in real time, allowing you to see important information and take immediate action.

Criteria that are consistent

Due to distractions, exhaustion, and boredom, human annotators make mistakes while classifying text data, and human subjectivity provides inconsistent standards. Machine learning, on the other hand, examines all data and outcomes through the same lens and criterion. Once correctly trained, a text classification model performs with unrivalled precision.

What Is Text Classification and How Does It Work?

Text classification can be done in two ways: manually or automatically.

A human annotator interprets the substance of the text and categorises it properly in manual text classification. This procedure can produce excellent results, but it is time-consuming and costly.

Machine learning, natural language processing (NLP), and other AI-guided approaches are used to automatically identify text in a faster, more cost-effective, and more accurate manner using automatic text classification.

We'll concentrate on automatic text classification in this guide.

Although there are numerous ways to automatic text classification, they all fall into one of three categories:

Rule-based systems
Machine learning-based systems
Hybrid systems
Rule-based systems

Rule-based techniques use a collection of customised language rules to classify text into ordered categories. These rules teach the system to find suitable categories based on the content of a text by using semantically relevant components of the text. An antecedent or pattern and a projected category make up each rule.

Let's say you wish to divide news stories into two categories: sports and politics. To begin, create two lists of words that characterise each group (e.g., words related to sports such as football, basketball, LeBron James, etc., and words related to politics, such as Donald Trump, Hillary Clinton, Putin, etc.).

When you're ready to classify a fresh incoming text, count the amount of sports-related words in the text, then do the same for politics-related phrases. The text is classed as Sports if the number of sports-related word appearances exceeds the number of politics-related word appearances, and vice versa.

For example, the headline "When is LeBron James' first game with the Lakers?" will be classified using this rule-based system. Because it only counted one sports-related term (LeBron James) and none of the politics-related terms, it was referred to as Sports.

Human-comprehensible rule-based systems can be enhanced over time. However, there are several drawbacks to this strategy. For begin, these systems necessitate extensive subject knowledge. They're also time-consuming, because creating rules for a complicated system can be difficult and sometimes necessitates extensive research and testing. Rule-based systems are particularly challenging to maintain and scale since adding new rules can alter the outcomes of previously applied rules.

Systems based on machine learning

Machine learning text categorization learns to create classifications based on past observations rather than depending on manually crafted rules. Machine learning algorithms may understand the varied correlations between pieces of text and that a specific output (i.e., tags) is expected for a specific input by using pre-labeled examples as training data (i.e., text). A "tag" is a pre-determined classification or category into which any text can be placed.

Feature extraction is the initial step in training a machine learning NLP classifier: a method is employed to convert each text into a numerical representation in the form of a vector. Bag of words is one of the most used ways, in which a vector indicates the frequency of a word in a predetermined lexicon of terms.

For example, if we defined our dictionary to include the terms "This is not the, awesome, bad, basketball," and we wished to vectorize the text "This is awesome," we would get the following vector representation: (1, 1, 0, 0, 1, 0, 0).

The machine learning algorithm is then fed training data, which is made up of pairs of feature sets (vectors for each text example) and tags (e.g. sports, politics) to create a classification model:

The machine learning model can begin to produce accurate predictions once it has been taught with enough training samples. The same feature extractor is used to convert unseen text into feature sets, which can then be input into the classification model to produce tag predictions:

Machine learning is usually significantly more accurate than human-crafted rule systems when it comes to text classification, especially for complicated NLP classification tasks. Machine learning classifiers are also easier to maintain, and you can always tag additional examples to learn new tasks.

Text Classification Algorithms Using Machine Learning

The Naive Bayes family of algorithms, support vector machines (SVM), and deep learning are some of the most prominent text categorization algorithms.

Naive Bayes

The Naive Bayes family of statistical algorithms are among the most widely employed in text classification and analysis.

Multinomial Naive Bayes (MNB) is one of those members, and it has the advantage of producing excellent results even when your dataset is small (a few thousand tagged samples) and computational resources are limited.

The Naive Bayes method is based on Bayes' Theorem, which allows us to calculate the conditional probabilities of two occurrences based on the probabilities of each individual event. So, for a given text, we calculate the probability of each tag and then output the tag with the highest probability.

The chance of A being true if B is true is equal to the probability of B being true if A is true divided by the probability of B being true.

This means that any vector representing a text must include information on the probabilities of particular words appearing in texts belonging to a given category, so that the algorithm can calculate the likelihood of that text belonging to that category.

Vector Support Machines (SVMs)

Support Vector Machines (SVM) are a sophisticated text categorization machine learning technique that, like Naive Bayes, requires little training data to begin producing correct results. SVM, on the other hand, requires more computing power than Naive Bayes, but the results are faster and more accurate.

SVM divides a space into two subspaces by drawing a line, or "hyperplane." Vectors (tags) that belong to a group are in one subspace, while vectors that do not belong to that group are in another.

The hyperplane with the greatest distance between tags is the best. In two dimensions, it appears as follows:

Those vectors are your training texts, and a group is a tag you've assigned to your texts.

Vectors/tags may not be able to be classified into only two groups as data becomes more complicated. So, here's what it looks like:

But that's what makes SVM algorithms so appealing: they're "multi-dimensional." As a result, the more complicated the data, the more accurate the results. Consider the above in three dimensions, with a Z-axis added to form a circle.

Reversed to a two-dimensional model This is how the ideal hyperplane looks:

Learning from the Ground Up

Deep learning, often known as neural networks, is a set of algorithms and approaches inspired by how the human brain functions. Deep learning architectures provide a lot of advantages for text classification since they can perform extremely well with low-level engineering and computation.

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are the two basic deep learning architectures for text classification (RNN).

Deep learning is a type of machine learning that employs numerous algorithms in a sequential sequence of events. It's comparable to how the human brain makes judgments, using multiple ways at the same time to process massive volumes of data.

Traditional machine learning techniques require a lot more training data than deep learning algorithms (at least millions of tagged examples). Traditional machine learning algorithms, such as SVM and NBeep learning classifiers, have a threshold for learning from training data, but they don't have one.

Deep learning approaches such as Word2Vec or GloVe are also used to increase the accuracy of classifiers learned with typical machine learning algorithms by obtaining better vector representations for words.

System Hybrids

Hybrid methods combine a machine learning-trained base classifier with a rule-based approach that improves the results even further. These hybrid systems can be easily fine-tuned by adding special rules for conflicting tags that the underlying classifier hasn't adequately described.

Metrics and Assessment

A common method for evaluating the performance of a text classifier is cross-validation. It operates by dividing the training data into equal-length example sets at random (e.g., 4 sets with 25 percent of the data). The remaining samples are used to train a text classifier for each batch (e.g., 75 percent of the samples). Following that, the classifiers generate predictions on their respective sets and compare the results to the human-annotated tags. This will show when a prediction was correct (true positives and true negatives) and when it was incorrect (false positives and false negatives) (false positives, false negatives).

These results can be used to create performance indicators that can be used to quickly analyse how effectively a classifier works:

Accuracy is the percentage of texts that were correctly categorised.
Precision: the percentage of examples the classifier correctly predicted for a given tag out of the total number of examples it predicted.
Recall: the proportion of examples the classifier predicted for a given tag out of the total number of examples it should have predicted for that tag out of the total number of cases it should have predicted for that tag.
The harmonic mean of precision and recall is the F1 Score.

What is the significance of text classification?

What are the benefits of using machine learning for text classification? Some of the more compelling reasons include:

Scalability

Analyses in real time

Criteria that are consistent

Text Classification Examples

Text categorization can be applied to a variety of situations, including categorising short messages (e.g., tweets, headlines, chatbot queries, etc.) and organising much longer papers (e.g., customer reviews, news articles,legal contracts, longform customer surveys, etc.). Sentiment analysis, subject labelling, language detection, and intent detection are some of the most well-known instances of text categorization.

Analysis of Public Opinion

The automated method of reading a text for opinion polarity (sentiment analysis or opinion mining) is perhaps the most well-known example of text categorization (positive, negative, neutral, and beyond). Sentiment classifiers are used in a variety of applications, including product analytics, brand monitoring, market research, customer service, workforce analytics, and more.

Sentiment analysis is a technique for automatically analysing all types of text for the writer's feelings and emotions.

To show how simple it is, use this pre-trained sentiment classifier with your own text.

Test with your own text

Results

TAGCONFIDENCE

Positive99.9%

Don't worry if you get an unexpected result; it's just because it hasn't been trained with similar expressions yet. Follow this simple sentiment analysis tutorial to develop a bespoke sentiment analysis model in just five steps for exceptionally accurate results customised to the exact language and criteria of your organisation.

Labeling by Subject

Topic labelling, or determining what a particular text is about, is another common example of text classification. It's frequently used for data structuring and organisation, such as sorting customer feedback by topic or news articles by subject.

Use this pre-trained model to categorise NPS replies for SaaS products based on their topic. Customer feedback is organised into four categories: Customer Service, Ease of Use, Features, and Pricing:

Test with your own text

Results

TAGCONFIDENCE

Customer Support84.5%

Learn how to create a custom multi-label text classifier and more about topic labelling.

Language Detection

Another wonderful example of text classification is language detection, which is the process of identifying incoming text according to its language. These text classifiers are frequently used in routing applications (e.g., route support tickets according to their language to the appropriate team).

This is a classifier that has been trained to recognise 49 different languages in text:

Test with your own text

Results

TAGCONFIDENCE

English-en100.0%

Detecting Intent

Another interesting use case for text classification is intent detection or intent classification, which examines text to determine the purpose for feedback. It could be a complaint or a customer expressing an interest in purchasing a product. Customer service, marketing email responses, product analytics, and corporate processes are all automated with it. Machine learning-based intent recognition can read emails and chatbot interactions and dispatch them to the appropriate department automatically.

Try out this email intent classifier, which has been trained to recognise the purpose of email responses. Interested, Not Interested, Unsubscribe, Wrong Person, Email Bounce, and Autoresponder: Interested, Not Interested, Unsubscribe, Wrong Person, Email Bounce, and Autoresponder: Interested, Not Interested, Unsubscribe, Wrong Person, Email Bounce

Test with your own text

Results

TAGCONFIDENCE

Interested100.0%

Applications and Use Cases for Text Classification

Text classification has dozens of applications and can be used for a variety of jobs. Data classification tools are sometimes used behind the scenes to improve app features that we use on a regular basis (like email spam filtering). Marketers, product managers, developers, and salespeople utilise classifiers to automate business operations and save hundreds of hours of manual data processing in other circumstances.

The following are some of the most common applications and use cases for text classification:

Identifying critical issues
Customer service processes can be automated.
Listening to the Customer's Voice (VoC)

Identifying Critical Issues

Every day, 500 million tweets are sent on Twitter alone.

According to polls, 83 percent of customers who leave a comment or make a complaint on social media expect a response the same day, with 18 percent anticipating it right away.

Businesses may make sense of vast amounts of data using techniques like aspect-based sentiment analysis to identify what people are talking about and how they're talking about each aspect with the help of text categorization. For example, a potential PR catastrophe, a consumer on the verge of churning, bug reports, or downtime affecting a large number of customers.

Processes for Customer Support Automation

One of the foundations of a sustainable and thriving company is providing a positive client experience. People are 93 percent more likely to be repeat consumers at organisations that provide exceptional customer service, according to Hubspot. According to the report, 80% of respondents indicated they have ceased doing business with a company due to a bad customer experience.

Text classification can assist support staff deliver exceptional service by automating jobs that are better left to computers, freeing up time for more critical work.

Text classification, for example, is frequently used to automate ticket routing and triaging. You can automatically route support tickets to a teammate with specific product knowledge using text classification. If a customer inquires about refunds, you can assign the ticket to a teammate who has the authority to issue refunds. This will ensure that the consumer receives a prompt and high-quality response.

Support teams can also use sentiment classification to determine the urgency of a support ticket and prioritise those with negative feelings. This can help you reduce client attrition and perhaps turn around a negative scenario.

Listening to the Customer's Voice (VoC)

Companies use surveys like the Net Promoter Score to hear from their consumers at every step of the process.

The data is both qualitative and quantitative, and while NPS scores are simple to assess, open-ended responses necessitate a more in-depth examination using text categorization techniques. Machine learning can swiftly handle open-ended consumer input instead of depending on humans to assess voice of customer data. Survey findings can be analysed using classification models to uncover trends and insights such as:

What do customers think of our product or service?
What can we do better?
What needs to be altered?

Teams can make more informed judgments by combining quantitative and qualitative insights rather to spending hours manually examining each open-ended response.

Resources for Text Classification

You may focus on other areas of your organisation after you start automating manual and repetitive jobs with various text classification algorithms.

But... where do you even begin with text classification? It's easy to become overwhelmed by the amount of material available on text analysis, machine learning, and natural language processing.

We make it simple for you to figure out where to begin at MonkeyLearn. We give a no-code text classifier builder that allows you to create your own text classifier in just a few minutes.

Before we go into more detail about what MonkeyLearn can do, let's have a look at what you'll need to make your first text classifier:

1. Sets of data

Without accurate training data, a text classifier is useless. Only by learning from prior experiences can machine learning algorithms produce accurate predictions.

You show an algorithm instances of properly tagged data, and it utilises that data to create predictions on text that has yet to be viewed.

If you want to anticipate the intent of chat talks, you'll need to find and collect chat conversations that represent the various intents you're looking for. If you use a different sort of data to train your model, the classifier will produce bad results.

So, how do you collect data for training?

Internal data created via CRMs (e.g. Salesforce, Hubspot), chat applications (e.g. Slack, Drift, Intercom), help desk software (e.g. Zendesk, Freshdesk, Front), survey tools (e.g. SurveyMonkey, Typeform, Google Forms), and customer satisfaction tools (e.g. SurveyMonkey, Typeform, Google Forms) can be used (e.g. Promoter.io, Retently, Satismeter). Most of these programmes allow you to export data as a CSV file, which you can then use to train your classifier.

Another alternative is to use external data from the web, which can be obtained by web scraping, APIs, or public datasets.

The datasets included below are publicly available and can be used to build your first text classifier and begin exploring right away.

Topic categorization:

The Reuters news dataset is arguably the most extensively used dataset for text categorization; it contains 21,578 Reuters news stories categorised with 135 topics, such as Politics, Economics, Sports, and Business.

Another famous dataset is 20 Newsgroups, which contains 20,000 papers across 20 different themes.
Analyzing public opinion:
Amazon Product Reviews is a well-known dataset that spans May 1996 to July 2014 and comprises 143 million reviews and star ratings (1 to 5 stars). An alternate dataset for Amazon product reviews may be found here.
IMDB reviews: a slightly smaller dataset from the Internet Movie Database containing 25,000 favourable and negative movie reviews (IMDB).
This dataset comprises approximately 15,000 tweets on airlines that are categorised as good, neutral, or negative.
Other often used datasets include:
Spambase is a database of 4,601 spam and non-spam emails.
Another dataset for spam identification is the SMS Spam Collection, which contains 5,574 SMS messages that have been classified as spam or legal.
Hate speech and offensive language: this dataset includes 24,802 tweets that have been classified and are divided into three categories: clean, hate speech, and offensive language.

2.Tools for Text Classification

Alright. Now that you have training data, you can use it to train a text classifier using a machine learning algorithm.

So, how do we go about doing this?

Many resources are available to assist you with the various stages of the process, such as converting texts to vectors, training a machine learning algorithm, and using a model to create predictions. In general, these instruments can be divided into two groups:

Libraries that are open-source
APIs for SaaS

Build vs. Buy is a never-ending discussion. Open-source libraries can compete with the best machine learning text classification tools, but they're expensive to develop and need years of data science and computer engineering expertise.

SaaS tools, on the other hand, need little to no coding, are entirely scalable, and are far less expensive because you simply utilise the tools that you require. Best of all, most can be installed right immediately and trained to execute just as quickly and accurately (often in only a few minutes).

Text classification open-source libraries

The abundance of open source libraries accessible for developers interested in implementing machine learning is one of the reasons it has become mainstream. Although they necessitate a solid understanding of data science and machine learning, these libraries provide a reasonable level of abstraction and simplification. Python, Java, and R all have a large number of actively maintained machine learning libraries with a wide range of features, performance, and capabilities.

Python for Text Classification

Python is typically the programming language of choice for machine learning model developers and data scientists. Python's popularity in the field can be attributed to its easy syntax, large community, and scientific-computing friendliness of its mathematical libraries.

One of the most popular libraries for general-purpose machine learning is Scikit-learn. It supports a variety of algorithms and features for working with text classification, regression, and clustering models that are both easy and efficient. If you're new to machine learning, scikit-learn is one of the most user-friendly libraries for learning how to classify text, with dozens of tutorials and step-by-step guidance available online.

NLTK is a prominent natural language processing (NLP) library with a large community behind it. It's particularly effective for text classification because it includes a variety of methods for making a machine read text, such as breaking down paragraphs into sentences, splitting up words, and recognising the part of speech of those words.

SpaCy, a toolkit with a more simple and straightforward approach than NLTK, is a current and newer NLP library. SpaCy, for example, just uses a single stemmer (NLTK has 9 different options). Word embeddings have also been added into SpaCy, which can aid improve text classification accuracy.

Deep learning frameworks like Keras, TensorFlow, and PyTorch can help you experiment with more advanced algorithms whenever you're ready. Keras is arguably the best place to start because it's meant to make building recurrent neural networks (RNNs) and convolutional neural networks (CNNs) easier (CNNs).

The most popular open source library for developing deep learning algorithms is TensorFlow. This library, which was developed by Google and is used by Dropbox, eBay, and Intel, is designed for building up, training, and deploying artificial neural networks with large datasets. It is the clear leader in the deep learning domain, while being more difficult to master than Keras. PyTorch, a comprehensive deep learning library primarily developed by Facebook and backed by Twitter, Nvidia, Salesforce, Stanford University, University of Oxford, and Uber, is a reliable alternative to TensorFlow.

Java Classification of Text

Java is another popular programming language for creating machine learning models. It, like Python, has a large community, a diverse environment, and a large number of open source machine learning and natural language processing libraries.

CoreNLP is the most widely used NLP framework in Java. It was developed by Stanford University and includes a text parser, a part-of-speech (POS) tagger, a named entity recognizer (NER), a coreference resolution system, and information extraction tools for comprehending human language.

OpenNLP is another prominent toolbox for natural language tasks. It's a set of linguistic analytic tools for text classification created by The Apache Software Foundation, including tokenization, sentence segmentation, part-of-speech tagging, chunking, and parsing.

Weka is a machine learning library created by the University of Waikato that includes a number of functions such as classification, regression, clustering, and data visualisation. It includes a graphical user interface for directly applying Weka's algorithms to a dataset, as well as an API for calling these algorithms from your own Java code.

R for Text Classification

The R programming language is a user-friendly programming language that is growing in popularity among machine learning enthusiasts. For statistical analysis, graphical depiction, and reporting, it has traditionally been most extensively used by academics and statisticians. It's the second most popular programming language for analytics, data science, and machine learning, according to KDnuggets (Python is the first).

R is a good choice for text categorization tasks because it offers a comprehensive, well-coordinated, and integrated set of data analytic tools.

Caret is a R package that allows you to create machine learning models. Its name stands for "Classification and Regression Training," and it provides a simple interface for implementing various algorithms as well as text classification tools such as pre-processing, feature selection, and model tweaking.

Mlr is another R package that provides a consistent interface for employing classification and regression algorithms, as well as the evaluation and optimization methods that go along with them.

Text Classification APIs as a Service

Open source tools are fantastic, but they are mostly aimed towards folks with a machine learning background. They also make it difficult to deploy and scale machine learning models, clean and curate data, tag training samples, do feature engineering, or bootstrap models.

Maybe you're thinking if there's a better way.

If you want to avoid these headaches, a fantastic alternative is to employ a text categorization Software as a Service (SaaS), which usually overcomes most of the issues stated above. Another advantage is that they don't require any prior machine learning knowledge, thus even non-programmers may use and consume text classifiers. When it comes to building your text classification system, leaving the heavy labour to a SaaS can save you time, money, and resources.

The following are some of the most impressive text classification SaaS solutions and APIs:

MonkeyLearn
Google Cloud Natural Language Processing
Watson is an IBM product.
Lexalytics \sMeaningCloud
Comprehend Amazon
Text Classification using Aylien

MonkeyLearn is an all-in-one text data analysis and visualisation tool that makes categorising text a breeze, whether you're looking at surveys, support issues, or reviews... You'll be able to see your results in stunning detail once you've ran your data through a variety of analytic tools.

In this lesson, we'll look at how to use sentiment and topic analysis to analyse and categorise a set of reviews. Follow along and then put our tools to the test.