What is Natural Language Processing?

What is Natural Language Processing?

Natural Language Processing (NLP) is a field of AI that focuses on understanding, interpreting, and responding to human text.

So when you ask something to Siri or Alexa, the software basically uses NLP to understand, process, and act on your command. For example – when you ask Siri, ” What’s the weather today?” It uses NLP to process your voice to understand the commands and then provide you with relevant weather information.

Why is NLP Important?

Let’s understand the reasons why natural language processing is crucial. There are many reasons why natural language processing is crucial, but we will be discussing only the most crucial ones.

Heightened Human-Machine Interactions

NLP allows you to create more intuitive and user-friendly interfaces. Thus, it will be easier for users to interact with the software, leading to increased interaction and adoption. It will also be very helpful in developing voice-controlled systems and text-to-speech apps.

Better Customer Support

Imagine having chatbot or visual assistant support that gives prompt, relevant, and helpful responses to each customer query. That’s what NLP can do for your business.

For example, suppose one of your customers used a chatbot to get information about a feature. With NLP, the chatbot can analyze the level of the request (customer emotion) and reply accordingly.

Related Read: Top Customer Support Tools to Consider

Understand Customer Emotions

The best way to improve your business offerings is to clearly understand what your customers are preferring. And the tools that help you with this often use NLP to evaluate emotions.

For example, tools like SurveySparrow integrate NLP into their software to identify and understand the underlying customer emotions. This way, you can categorize and prioritize customer feedback (or review), work on the issues, and improve your business.

Learn more about how SurveySparrow has leveraged AI and NLP.

Analyze Any Language Text

Analyzing text can be challenging, especially when you are running a global service. But not with NLP-powered tools. It’ll translate text (and speech) in real-time, allowing you to do better and faster analysis.

As a result, it facilitates seamless global communication and helps you reach a wider audience.

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What are Natural Language Processing Techniques?

With all the things discussed by now, you might be wondering how natural language processing works. Well, that’s exactly what we are going to discuss in this section. The following NLP techniques are used to analyze, understand, and generate human language.

Technique 1 – Tokenization

This is where the larger text is broken down into smaller units, which include words, phrases, or sentences.

For example, a line containing “Natural language processing” becomes – “Natural”, “language”, and “processing”.

Technique 2 – POS Tagging

POS or Part-of-Speech tagging is where it assigns grammatical categories to each token. This includes but is not limited to nouns, verbs, and adjectives.

For example, consider the sentence – ” The cat sat on the mat”. Here, the tagging will go something like – (“The”, “DT”), (“cat”, “NN”), (“sat”, “VBD”), (“on”, “IN”), (“the”, “DT”), and (“mat”, “NN”).

• DT* – Determiner
• NN* – Noun
• VBD* – Verb
• IN* – Prepositions

Technique 3 – Named Entity Recognition

Named Entity Recognition or NER is where it identifies and classifies entities in text. This can be anything from people and organization to location and dates.

Example: The text “Dwayne Johnson was born in Hawaii” becomes (“Dwayne Johnson”, PERSON), (“Hawaii”, PLACE).

Technique 4 – Sentiment Analysis

This is where it understands and assigns accurate emotions to texts. The emotions can be positive, neutral, or negative.

An example here is understanding the negative emotion in the following text. “It has been a week, and I still haven’t got my refund yet…disastrous service”.

Here are some sentiment analysis tools that use NLP.

Technique 5 – Stemming and Lemmatization

Stemming is the process of removing any prefixes or suffixes from the word. At the same time, Lemmatization considers the context in which the word is used and then converts it to root form. In general, this technique reduces words to their base or root form.

For example, the word “running” will become “run”.

Technique 6 – Parsing

To understand the relation between the words and the grammatical structure, that’s what parsing is for. For example, parsing the sentence, “The cat sat on a mat”, reveals its syntactic structure.

Technique 7 – Machine Translation

This is where the translation of text from one language to another happens. So, translating text from Espanol to English (or any other language) is easier and faster.

Technique 8 – Text Summarization

It’s possible that you get large volumes of text that are very hard to analyze and understand. This process shortens these large volumes into shorter summaries, keeping the key information intact.
Converting a large paragraph into focused bullet points is one way to look at this.

Technique 9 – Topic Modeling

Topic modeling helps understand the main topics or themes that are discussed. For example, suppose you have run a survey and got back around 500 responses. With topic modeling, you could easily identify which was the most discussed topic among these 500 responses.

What are the Different Natural Language Processing Models

NLP models are systems used to perform specific language-related tasks using AI and ML. Let’s have a look at some of the top and well-known natural language processing models.

Model 1 – BERT

BERT is short for Bidirectional Encoder Representations from Transformers. It’s designed to understand the context of a word in search queries. The model uses a transformer architecture to process words in relation to all other words in a sentence.

Application: Text classification, question answering, sentiment analysis, NER.

Who uses this model? Google for enhancing their search queries.

Model 2 – GPT

Everyone knows ChatGPT; it’s the same GPT. GPT stands for Generative Pre-trained Transformer. These NLP models generate human-like text based on input. Since the models are pre-trained, the application can be a bit limited.

Application: Text, generation, language translation, summarization, dialogue systems.

Who uses this model? Open AI’s ChatGPT.

Model 3 – T5

Text-to-Text Transfer Transformer (T5) treats every NLP task as a text-to-text problem. Therefore, regardless of the task, it converts input text into output text.

Application: Translation, summarization, text classification, question answering.

Who uses this model? Google Translate.

Model 4 – XLNet

This NLP model combines the strengths of autoregressive language models and autoencoding. As a result, it has improved the performance over BERT on several benchmarks.

Application: Text classification, language modeling.

Who uses this model? Social media monitoring tools for sentiment analysis.

Model 5 – RoBERTa

A Robustly Optimized BERT pertaining Approach (RoBERTa) is basically an improved version of BERT. It uses more data and training for longer periods with dynamic masking. Thus, it offers improved performance for NLP tasks.

Application: NER, text classification, question answering.

Who uses this model? Facebook’s Hate Speech Detection.

Model 6 – ERNIE

Enhanced Representation through Knowledge Integration (ERNIE) is developed by Baidu. It integrates knowledge graphs into pertaining, enhancing language understanding. This improves the contextual understanding of the model.

Application: Sentiment analysis, information retrieval, question answering.

Who uses this model? Baidu Search Engine

Model 7 – Turing-NLG

Turing-NLG is one of the largest language models (LLMs) designed for natural language generation. It’s best for interpreting a significantly large number of parameters. Thus it leads to high-quality language generation.

Application: Text generation, conversation modeling, summarization.

Who uses this model? Microsoft Office.

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7 Natural Language Processing Use Cases

Natural language processing offers a wide range of use cases. Among the vast use cases, we will be discussing only seven crucial ones here. So, let’s have a look at them.

NLP in Customer Support

Application: Chatbots and Virtual Assistants

This is something we have already discussed in the earlier section. NLP-powered chatbots can provide instant, relevant, and helpful responses to customer queries. Not only that, but also it can handle routine queries and resolve common issues.

Therefore, being available 24*7 has new meaning. It’s not just about being there but also resolving issues faster. This leads to reduced wait times and freeing your employees to focus on more complex problems.

Example: Chatbots on eCommerce platforms to track orders or answer product-related questions.

NLP in Healthcare

Application: Medical Record Analysis

NLP can be very helpful in extracting valuable patient information from clinical notes and research papers. It further helps by identifying treatment options and monitoring patient outcomes by analyzing unstructured data.

Example: IBM Watson Health uses NLP to analyze large amounts of medical and patient data.

Helpful Read: Top patient experience tools that use NLP

NLP in Finance

Application: Market Analysis

Will the stock go up or down? What would be the trend? All these kinds of questions are redundant with NLP in finance.

Analyze data in bulk, including news articles, financial reports, and social media posts. These will give you a better understanding of the market sentiment. Therefore, you (traders and investors) can make better-informed decisions.

Example: Trading platforms use this to track positive or negative mentions of companies and stocks.

NLP in Search Engine

Application: Improved Search Results

Ever wonder how Google is able to provide you with the most relevant search results? It’s not just SEO, it’s NLP in working.

It helps search engines understand the context and intent of the query. As a result, it can retrieve more accurate and relevant search results.

Example: BERT in Google

NLP in Social Media Monitoring

Application: Content Moderation

NLP is used by social media platforms to monitor and find harmful or offensive content. If you are someone who is active on X or Facebook, it’s possible you have heard about the platform removing content from their platform. This is how they find and remove that content.

Example: Facebook’s Hate Speech Detection

NLP in Translation

Application: Language Translation

Most of the language translator apps employ NLP. This is a more efficient and quicker way to translate the text from one language to another. NLP allows for accurate translation, helping users to understand and communicate better.

Example: Google Translate

NLP in Voice Assistants

Application: Speech Recognition and Command Execution

NLP powers speech recognition systems for better performance. It converts spoken language into text and analyzes it to understand the voice command. With voice activation and searches growing NLP can be the pivotal tech for performing tasks based on verbal instructions.

Example: Alexa from Amazon

NLP is also used across other industries such as education, legal, HR, and more. But, at its current state, its most useful use cases are the above-mentioned seven of them.

How SurveySparrow Uses Natural Language Processing?

As you may already know, SurveySparrow is one of the best survey software available in the market. It can process large amounts of customer data and make sense of them in a snap. How does it do it? A mix of NLP and AI.

Its unique feature – CogniVue – can process vast amounts of data and provide you with crucial insights that can help you improve your overall experience. Some of the key insights you can expect are as follows.

Sentiment Analysis

NPS tools may be able to help you understand overall customer emotions, but CogniVue offers a much more comprehensive version. By analyzing the open-ended questions it identifies the underlying customer emotions across each feedback. Thereby giving you a better idea of customer perception.

Key Driver Analysis

Learn what’s boosting and dragging your business. Key driver analysis provides you with factors that add strength to your business’s success and also points out those that need improvement.

Topics and Keywords

Define the topics you want to focus on and see how frequently your customers talk about them. This is the best way to know whether your business is achieving what you intend to achieve.

Similarly, you can identify the most used keywords by the customers about your brand. These keywords will shine insight into how your customers perceive your brand.

Intrigued? Want to know more about the feature? Well, feel free to book a demo call with our team. The feature is only available on request and is for exclusive users like you.

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