TECHNOLOGY
Natural Language Processing
Understand your customers like never before with Natural Language Processing
What is Natural Language Processing?
Imagine being able to analyze vast amounts of text data, from customer reviews to social media conversations, and gain valuable insights into your audience's needs and preferences. That's Natural Language Processing (NLP).
It is a field of artificial intelligence that enables machines to understand, interpret, and respond to human language. It bridges the gap between human communication and computer understanding. For example, when you use voice assistants like Siri or Alexa, NLP is at work. And it’s just gaining ground as the NLP market is expected to reach USD 35 billion by 2026.
How does it work?
01
Data collection
NLP starts with preparing the text data for analysis:
Tokenization: Breaking down the text into smaller units like words or sentences.
Normalization: Converting text to a consistent format, such as lowercase, to ensure consistency for analysis.
Stemming or lemmatization: Reducing words to their base form (e.g., "running" becomes "run") to improve accuracy.
Removing stop words: Eliminating common words like "the" and "a" that don't contribute much meaning.
02
Feature engineering
Creating features from the text data that can be used by machine learning algorithms:
Word frequency: How often a particular word appears in the text.
N-grams: Sequences of consecutive words (e.g., "big data") that can capture the context of language.
Part-of-speech tags: Identifying the grammatical function of each word (e.g., noun, verb, adjective).
03
Applying NLP techniques
Depending on the specific NLP task, the system uses different algorithms or models:
Sentiment analysis: Classifying text as positive, negative, or neutral. This can be used to analyze customer reviews or social media posts.
Named entity recognition (NER): Identifying and classifying named entities in text (e.g., people, organizations, or locations). This can be useful for information extraction tasks
Machine translation: Automatically translating text from one language to another.
Text summarization: Condensing a lengthy piece of text into a shorter summary that retains the key points.
04
Evaluation and refinement
Evaluating the performance of the model and making adjustments as needed to improve its accuracy
How businesses are
using NLP
NLP changes entire industries. It automates processes, extracts insights from unstructured data, and enhances decision-making.
Healthcare
AI technology in general (including NLP), could save the US healthcare economy USD 150 billion annually.
IBM Watson Health uses NLP to analyze medical literature and patient records, aiding doctors in diagnosing and recommending personalized treatment plans.
Use cases:
- Enabling better patient care and operational efficiency
- Analyzing unstructured medical data
- Extracting meaningful insights
- Improving clinical documentation
Healthcare
AI technology in general (including NLP), could save the US healthcare economy USD 150 billion annually.
IBM Watson Health uses NLP to analyze medical literature and patient records, aiding doctors in diagnosing and recommending personalized treatment plans.
Use cases:
- Enabling better patient care and operational efficiency
- Analyzing unstructured medical data
- Extracting meaningful insights
- Improving clinical documentation
Finance
AI applications (including NLP) could save North American banks USD 447 billion through cost reductions and increased productivity.
JP Morgan Chase employs NLP in its Contract Intelligence (COiN) platform to review legal documents. This way, the company reduced the time needed to just seconds.
Use cases:
- Risk management
- Fraud detection
- Customer service automation
- Identifying trends and predicting market movements.
Retail
Companies that integrate NLP for personalized content or responses into their business operations can significantly improve customer satisfaction and drive 40% more sales.
Amazon uses NLP to analyze customer reviews and feedback, optimize product recommendations, and improve customer satisfaction.
Use cases:
- Personalized recommendations
- Sentiment analysis
- Chatbots for customer service
Manufacturing
AI [+NLP] could contribute USD 15.7 trillion to the global economy by 2030 through improved efficiency and automation.
GE Aviation uses NLP to predict when airplane parts might fail, preventing costly downtime and ensuring safety.
Use cases:
- Improving supply chain management
- Predictive maintenance
- Quality control
The core NLP techniques
Technique
What it means
Goal
Practical application
Text Analysis
More Insights
Extracting meaningful information from unstructured text data.
Identify patterns, trends, and insights from text data. Transform raw text data into a structured format suitable for further analysis by machine learning algorithms.
- Analyzing customer feedback
- Monitoring social media
- Processing legal documents
Text Analysis
More Insights
Evaluates and interprets emotions expressed in text data.
It classifies text as positive, negative, or neutral.
Understand the underlying sentiment behind user reviews, social media posts, or customer feedback.
Can significantly impact customer satisfaction.
- Customer service to gauge satisfaction
- Monitor brand reputation
- Analyze social media sentiments
Language Translation
More Insights
Converts text from one language to another using machine learning models.
Break down language barriers and ease communication.
- Real-time translation services
- Multilingual customer support
- Content localization
Speech Recognition
More Insights
Converts spoken language into text using machine learning algorithms.
Enable voice-activated interfaces, improve accessibility.
Some speech recognition systems can achieve word error rates as low as 5%.
- Virtual assistants like Siri and Alexa
- Transcription services
- Automated customer service
Chatbots
More Insights
AI-powered programs that simulate human conversation to interact with users.
Automate customer service and provide instant responses.
- Customer support
- Deliver targeted marketing messages and recommendations based on user interactions
- Qualify leads and collect customer information through chatbot interactions
Increase CSAT by at least 10% by analyzing customers’ pain points with NLP
Let’s discuss how we can help you achieve your goals effortlessly
and affordably.
Future of NLP
Get custom AI solutions covering your unique business needs
Questions & answers
What is Natural Language Processing and how can it benefit my business?
NLP is a branch of artificial intelligence. It enables machines to understand, interpret, and respond to human language. NLP automates customer support, enhances data analysis, improves customer interactions, and streamlines operations.
How difficult is it to integrate NLP into my existing systems?
NLP solutions come in various forms, from user-friendly cloud-based platforms to custom-built models. Businesses always have options. DevicoAI offers comprehensive support to ensure a smooth integration process, tailored to your business needs.
How secure is my data when using NLP solutions
NLP platforms incorporate robust security measures, such as data encryption, anonymization, and compliance with industry standards (e.g., GDPR, HIPAA). We use abiding security protocols to ensure that your data is protected throughout the NLP implementation process.
What if I'm not sure how NLP can benefit my specific industry?
Above we specified only a few industries where you can apply for NLP. Others are no exceptions. We can discuss your specific challenges and goals to identify how NLP can be used to gain valuable insights from text data and improve customer interactions.
How do I measure the effectiveness of NLP in my business?
We measure NLP's effectiveness by tracking key performance indicators (KPIs) relevant to your specific use case. We work with you to establish clear metrics and provide detailed analytics to monitor and optimize the performance of NLP solutions.
Experience the future, now