Natural language processing: Tools, examples, and techniques

Natural language processing (NLP) is a part of Artificial Intelligence that makes it possible for computers to understand, read, manipulate and interpret human language.

What is natural language processing?

In general, natural processing language allowed computers the ability to read, measure sentiment and determine the vital parts of human languages. Natural language processing integrated into systems development such as speech recognition, machine translation, document summarization, chatbots, spam detection, question answering, predictive typing, and so on.
Natural language processing tools

Choosing natural language processing tools

SaaS tools or open-source libraries are integrated to offer a free and flexible solution to customize fully natural language processing tools. Most of the tools are designed for ready-to-use, with powerful cloud-based and community-driven frameworks. This article would illustrate a list of the best natural language processing tools:

1. MonkeyLearn is user-friendly that allows software developers to gain valuable insights from text data, connect text analysis models to most favorite apps like Google Sheets, Excel, Zendesk)
2. Aylien: By using deep learning, natural language processing can analyze large volumes of text-based data, article extraction, sentiment analysis, and text summarization.
3. IBM Watson: Allows software engineers to identify and extract keywords, emotions, entities, and categories, also have been applied in various industries from healthcare to finance.
4. Google Cloud provides many pre-trained frameworks for sentiment analysis, content classification, and entity extraction. Furthermore, software companies can build customized machine learning models.
5. Amazon Comprehend: A natural language processing technique integrates with Amazon Web Services infrastructure, which uses in sentiment analysis, topic modeling, and entity recognition. This tool is considered the best performance in the healthcare industry, with Amazon Comprehend Medical, which allows app developers to perform advanced medical data analysis.
6. Natural Language Toolkit (NLTK): NLTK is one of the leading tools in Natural Language Processing techniques and has a robust and active community, offering a wide range of tutorials for language processing, sample datasets, and resources.
7. Stanford Core NLP: Which is written in Javascript, allows software developers to perform a variety of natural language processing technologies such as tokenization, named entity recognition to handle complex tasks in the software development processes.
8. TextBlob: It is the newest tool with lightning-fast, easy to use, well-documented, which can handle large volumes of data, and apart from others, SpaCy keeps the algorithms in a short menu and brings the best available option for each task.
9. SpaCy: It is the newest tool with lightning-fast, easy to use, well-documented, which can handle large volumes of data, and apart from others, SpaCy keeps the algorithms in a short menu and brings the best available option for each task.
10. GenSim: It is designed for dealing with topic modeling using algorithms. Also, GenSim is excellent at indexing texts, text similarity detection, and document classification.

Natural language processing examples in several industry

Benefits of natural language processing appear in many mobile app advancements and web application development stages, and being innovative and cutting-edge software development, it has become a solution of application everyday use thanks to the following supports below:

• Text Analysis includes morphological, grammatical, and syntactic analysis, which advantages in analyzing keywords, topics, locations, times for users.
• Autocomplete Take Google, a searching engine tool as an example of natural language processing, offer prediction results for users by considering the few initial characters of words to help reduce the complex queries to get faster results.
• Autocorrect By following an algorithm, this Natural language processing software can analyze the closest term to the misspelling words then accurate it immediately.
• Smart Search With the integration of natural language processing software, developers can gather context and contextually relevant synonyms to predict customer insights more accurately.
• Chatbots With the support of natural language processing tools and machine learning, chatbots can understand the user's requirements. Also, Chatbot can automatically recommend products and services to users. Translations Tools - its NLP solutions play a vital role in Google Translate. It can analyze and understand the phrases or terms its users are translating.

Why do businesses need natural language processing solutions?

Natural language processing in business provides benefits in delivering lucrative data and solutions with lower expenses. Additionally, it brings enormous revenues to the market. According to Statista, the value is predicted to increase from 12 billion U.S. dollars in 2020 to 43 billion in 2025. Let's explore more reasons why businesses prefer processing natural language:

Greater user experience Natural language processing provides more user-friendly interactions with the customer such as spell check, autocorrect, which improve the accuracy of documentation, the more beneficial the software brings to the customer, the more retention of them to the software.

Automate support With natural language processing in AI, the system can cope with human interaction automatically. Also, allow users to navigate to support articles, manage accounts, and order products and services.

Descriptive Analytics: Processing natural language allows businesses to review the potential customers for the product or service they desire. By collecting customer reviews and Feedback, companies can analyze and show the frequency of individual advantages and disadvantages.

Monitor and Analyze Feedback natural language processing software enables users to aggregate customer feedback from many different channels. AccordinglyAccordingly, it helps with strategic planning.

Email filters: natural language processing software can uncover specific words or phrases that determine a spam message, the software immediately flags the email and moves it to the spam box. Gmail is the system that uses natural language processing which keeps the inbox to an organizable size with meaningful and relevance that people wish to review and respond to immediately.

Save time and money: Take chatbot as a natural language processing example, customer support teams are no longer needed because chatbot can help automate customer support operations.

Increase employee satisfaction: Natural language processing solutions are built to handle most time-consuming, ordinary, or routine tasks such as answering repetitive questions, spelling checks, classifying emails, moderating comments, and Feedback.

In conclusion

The benefits of natural language processing are unreliable and are becoming more popular and closer to everyday life. The continuous advancement in natural language processing technologies seemingly improves accuracy and speed. This technology not only supports businesses enhancing user experiences, but also helps computers to understand and communicate with people through their language. Hence, the high demand to integrate natural language processing in mobile app and web application create opportunities for software outsourcing companies and high-demand for dedicated developers.