Have you ever wished to know more about natural language processing (NLP) algorithms?
Are you intrigued by how NLP works to help data analysts gain valuable insights from customer data?
With the recent advancements in artificial intelligence (AI) and machine learning, understanding how natural language processing works is becoming increasingly important.
In this guide, we’ll discuss what NLP algorithms are, how they work, and the different types available for businesses to use.
We’ll cover:
- What is natural language processing (NLP)?
- What are NLP algorithms?
- Types of NLP algorithms
- Sentiment analysis
- Keyword extraction
- Knowledge graphs
- Word clouds
- Text summarization
- How to get started with NLP algorithms
- NLP algorithms FAQs
- Wrap-up
Ready to learn more about NLP algorithms and how to get started with them? Let’s dive in.
1. What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a branch of AI that focuses on developing computer algorithms to understand and process natural language.
It allows computers to understand human written and spoken language to analyze text, extract meaning, recognize patterns, and generate new text content.
To fully understand NLP, you’ll have to know what their algorithms are and what they involve. Next, I’ll provide a simple explanation of them.
2. What are NLP algorithms?
NLP algorithms are complex mathematical formulas used to train computers to understand and process natural language. They help machines make sense of the data they get from written or spoken words and extract meaning from them.
Put in simple terms, these algorithms are like dictionaries that allow machines to make sense of what people are saying without having to understand the intricacies of human language.
NLP algorithms use a variety of techniques, such as sentiment analysis, keyword extraction, knowledge graphs, word clouds, and text summarization, which we’ll discuss in the next section.
3. Types of NLP algorithms
To help achieve the different results and applications in NLP, a range of algorithms are used by data scientists.
Some of the common types of NLP algorithms in data science include:
Sentiment analysis
Sentiment analysis is the process of classifying text into categories of positive, negative, or neutral sentiment.
It works through the use of several techniques:
1. Tokenization
This is the first step in the process, where the text is broken down into individual words or “tokens”. Each token is then analyzed separately.
2. Stop words removal
Stop words such as “is”, “an”, and “the”, which do not carry significant meaning, are removed to focus on important words.
3. Text normalization
Words are converted to their base or root form. For example, “running” might be reduced to its root word, “run”. This is known as Stemming or Lemmatization.
4. Feature extraction
Key features or words that will help determine sentiment are extracted from the text. These could include adjectives like “good”, “bad”, “awesome”, etc.
5. Classification
The sentiment is then classified using machine learning algorithms. This could be a binary classification (positive/negative), a multi-class classification (happy, sad, angry, etc.), or a scale (rating from 1 to 10).
However, sarcasm, irony, slang, and other factors can make it challenging to determine sentiment accurately.
Nonetheless, it’s often used by businesses to gauge customer sentiment about their products or services through customer feedback.
Keyword extraction
Keyword extraction is a process of extracting important keywords or phrases from text.
This algorithm extracts meaningful keywords from text to help identify the topics or trends. It can be used to identify topics in documents, blog posts, and web pages.
This can be further applied to business use cases by monitoring customer conversations and identifying potential market opportunities.
It’s also typically used in situations where large amounts of unstructured text data need to be analyzed.
Knowledge graph
This algorithm creates a graph network of important entities, such as people, places, and things. This graph can then be used to understand how different concepts are related.
A knowledge graph is a key algorithm in helping machines understand the context and semantics of human language. This means that machines are able to understand the nuances and complexities of language.
Word cloud
This one most of us have come across at one point or another! A word cloud is a graphical representation of the frequency of words used in the text. It can be used to identify trends and topics in customer feedback.
Just in case, here’s a word cloud composed using the text from this article as the data:
Word clouds are commonly used for analyzing data from social network websites, customer reviews, feedback, or other textual content to get insights about prominent themes, sentiments, or buzzwords around a particular topic.
They’re commonly used in presentations to give an intuitive summary of the text.
Text summarization
This algorithm creates summaries of long texts to make it easier for humans to understand their contents quickly. Businesses can use it to summarize customer feedback or large documents into shorter versions for better analysis.
Using these algorithms, data professionals can perform common data analytics tasks used in businesses.
3 common use cases for NLP algorithms
To give you a better idea of what these algorithms can offer for business applications, here are three common use cases for NLP algorithms:
- Customer support: Businesses can use sentiment analysis to monitor customer feedback and identify areas of improvement.
- Market analysis: Keyword extraction can help businesses identify topics and trends in customer conversations to inform their marketing strategies.
- Text summarization: Businesses can use text summarization to quickly analyze long documents or customer feedback.
These are just a few of the ways businesses can use NLP algorithms to gain insights from their data.
4. How to get started with NLP algorithms
Interested to try out some of these algorithms for yourself? Here are a few steps on how to get started.
Step 1: Determine your problem
Before you start, it’s important to define your business problem.
This involves asking questions like:
- What data do you have?
- Which insights are you looking for?
Try to be as specific as possible. This will help with selecting the appropriate algorithm later on.
Step 2: Identify your dataset
The next step is to identify your dataset. Depending on the problem you are trying to solve, you might have access to customer feedback data, product reviews, forum posts, or social media data.
Step 3: Data cleaning
Once you have identified your dataset, you’ll have to prepare the data by cleaning it.
Data cleaning involves removing any irrelevant data or typo errors, converting all text to lowercase, and normalizing the language. This step might require some knowledge of common libraries in Python or packages in R. If you need a refresher, just use our guide to data cleaning.
You can use NLP libraries such as NLTK or spaCy to help clean your data.
Some other common tools for cleaning data include:
These are just among the many machine learning tools used by data scientists.
Step 4: Select an algorithm
For your next step, you’ll need to select an algorithm. This will depend on the business problem you are trying to solve. You can refer to the list of algorithms we discussed earlier for more information.
Once you have identified the algorithm, you’ll need to train it by feeding it with the data from your dataset.
You can use the Scikit-learn library in Python, which offers a variety of algorithms and tools for natural language processing.
Step 5: Analyze output results
The last step is to analyze the output results of your algorithm. Depending on what type of algorithm you are using, you might see metrics such as sentiment scores or keyword frequencies.
Here are some common metrics used to evaluate outputs:
- Precision: Measures how accurate the algorithm is at correctly classifying data.
- Recall: Measures how much of the relevant data was correctly classified.
- F1 score: Measures balance between precision and recall.
You can also use visualizations such as word clouds to better present your results to stakeholders.
5. NLP algorithms FAQs
Which programming language is best for NLP?
Python is the best programming language for NLP for its wide range of NLP libraries, ease of use, and community support. However, other programming languages like R and Java are also popular for NLP.
Is NLP high-paying?
Yes, NLP data scientists are paid high-paying salaries. According to PayScale, the average salary for an NLP data scientist in the U.S. is about $104,000 per year.
Can Python be used for NLP?
Yes, Python can be used for NLP. It’s the most popular due to its wide range of libraries and tools. It is also considered one of the most beginner-friendly programming languages which makes it ideal for beginners to learn NLP.
6. Wrap-up
NLP algorithms can sound like far-fetched concepts, but in reality, with the right directions and the determination to learn, you can easily get started with them.
We hope this guide gives you a better overall understanding of what natural language processing (NLP) algorithms are. To recap, we discussed the different types of NLP algorithms available, as well as their common use cases and applications.
If you’re interested in getting started with NLP algorithms, check out CareerFoundry’s free, 5-day data analytics short course. For more related information on data analytics and generative AI, do check out the following articles:
- A Beginner’s Guide to Machine Learning Models
- 11 of the Best ChatGPT Data Analysis Prompts You Should Know
- What’s the Average Machine Learning Engineer Salary?
