A field of artificial intelligence (AI), natural language processing (NLP) trains machines to understand and use human language.
Today, NLP is already a part of everyday life for many organizations, powering their search engines, prompting chatbots for customer service, voice-operated GPS systems, and digital assistants on smart devices. From the communication skills of large language models (LLMs) to the ability of image generation models, NLP analysis has enabled the era of generative AI.
Natural Language Processing - Definition
A subfield of artificial intelligence (AI), natural language processing or NLP uses machine learning to help computers understand and communicate with human language. It enables digital devices to recognize, understand, and generate text and speech by combining computational linguistics with statistical modeling, machine learning (ML), and deep learning.
From machine translation, summarization, and spell check, the natural language processing (NLP) framework helps machines to process and understand human language and automatically perform repetitive tasks.
NLP plays a growing role in enterprise solutions, enabling them to streamline and automate their business operations, increase employee productivity, and simplify critical business processes.
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Natural Language Processing Tools
Natural language processing (NLP) allows machines to understand, interpret, and produce human speech. Its main goal is to authorize human machines to communicate in natural human language. Natural language processing tools need to understand context, nuances of language, and even jokes and sarcasm to engage in a conversation. Furthermore, only large language models (LLMs) are able to perform these very complex tasks.
Natural language processing tools are software applications that help process and analyze text or speech in standard conversation format. Natural language processing tools equip users to interact with artificial systems without needing to input software code or any complex commands.
In data analytics, natural language processing tools are used to improve the narrative of data visualization services, enabling non-technical team members to interact with data, manage analysis results, search for information, and help identify and mine texts. To accomplish these tasks, natural language processing tools leverage machine learning algorithms, linguistic rules, and statistical techniques. These tools offer a significant competitive advantage to organizations that effectively use them.
Natural Language Processing Tools - Importance
Natural language processing tools help improve knowledge management within an organization by producing meeting summaries and notes. This information is easily accessible to all team members.
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Automate document processing
Natural language processing (NLP) tools can be used to perform complex tasks like document processing, thereby saving time and increasing productivity. Document automation saves significant staff time and frees employees from complex and redundant tasks. The process begins with seamlessly copying data from paper forms to customer service functions. However, automation in natural language processing uses AI, speech recognition (STT), and speech and text processing to create meeting summaries and notes.
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Early assessment
Natural language processing tools assist organizations in identifying urgency in writing. They customize the NLP-based emergency recognition system and train it to identify certain words and phrases that indicate dissatisfaction. It further allows businesses to prioritize important customer requests, thus improving lead times and offering greater customer satisfaction.
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Natural Language Processing Tools - Benefits
A natural language processing system works rapidly and efficiently. After NLP models are trained, they can perform administrative tasks, freeing staff for more productive work. The benefits of natural language processing tools include:
- Faster insight discovery: Organizations can uncover hidden patterns, trends, and relationships between different content. Text data retrieval supports deeper insights and analysis, enabling users to make better-informed decisions.
- Greater savings: With the massive volume of unstructured text data available, NLP can help automate the gathering, processing, and organization of data with less manual effort.
- Quick access to corporate data: An enterprise can build a knowledge base of organizational information that can be efficiently accessed with AI search. NLP can help in returning relevant information to enhance customer service and close sales.
The powerful benefits of natural language processing services are not limited to insights. In today's digital environment, every aspect of management uses NLP-based applications and software. A mix of NLP and AI development is helping every business to navigate the ever-evolving market landscape.
Best Natural Language Processing Tools in 2025
Natural language processing tools play a key role in language translation, text summarization, fraud detection, and answering questions. They are a great help for authentic information. Let's explore some of the top tools for natural language processing in 2025:
IBM Watson
A cloud-based platform, IBM Watson Natural Language Understanding (NLU) uses advanced artificial intelligence and natural language processing (NLP) techniques to analyze and understand data. The tool can extract critical insights from unstructured text, such as keywords, sentiment, and categories. IBM Watson NLU is used across various applications, including social media monitoring, customer feedback analysis, and content categorization. The tool is well-suited for organizations that require advanced text analytics capabilities to enhance their decision-making and gain a better understanding of customer feedback and market trends.
Features:
- The tool uses machine learning and Watson Assistant as a chat box.
- It is a blockchain technology with a focus on protection standards.
- The tool is helpful in understanding concepts and extracting metadata.
Pros:
- It helps in decision-making and analysis systems for available data.
- It has the advantage of sustainability and competitive quantities.
Cons:
- The tool requires maintenance and constant human checks.
- It requires a lot of steps to structure data.
Microsoft Azure
Azure Cognitive Service for Language delivers conversational language understanding, enabling users to build a component to be used in an end-to-end conversational application. Through the program, users can design a conversational bot to help with customer engagement and control an application that operates in a speech-to-text function. Microsoft Azure is a leading AI on NLP with cloud storage features that process diverse applications within. It acts as a text analyst with sentiment analysis and speech recognition.
Features:
- The tool supports the development of various applications, including Azure AQL.
- It has scalable solutions that optimize costs.
Pros:
- The tool is cost-effective and a good cloud provider.
- It has high data security.
Cons:
- The tool requires extensive management and platform expertise.
- It has some storage limitations.
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MonkeyLearn
MonkeyLearn is considered a solution that helps a person extract data from any Gmail, tweets, or sentence in written form. The extracted data can be converted into visualization to be presented to the user for picture-directed work.
Features
- MonkeyLearn has a graphic user interface.
- The tool is commonly used in sentiment analysis and classification of topics.
- The tool does not require any coding or data science knowledge.
Pros:
- The platform provides a tutorial on how to use the tool, which is beneficial.
- Sample templates are provided.
Cons:
- The platform is limited to a certain number of functions.
- The decision in the classification of emotions is not enhanced.
spaCy
spaCY is an open-source library for NLP. The Python-based library tool offers language support for almost 72 languages across transformer-based pipelines at an efficient speed. The latest version of the tool provides new training templates for projects so that users can define their own custom models. This platform helps extract information and offers it to NLP. The tool uses rule-based and machine-learning approaches, making it more accessible to handle.
Features:
- The tool can convert segmented text into words and punctuation marks.
- It possesses the ability to compare words, text, and documents and check their similarity.
- Labeling syntactic dependencies includes assigning labels.
Pros:
- It simplifies natural language processing.
- The tool is easy to use, and the extraction of insight is accurate.
Cons:
- The platform is considered less flexible and lacks pre-trained models.
- The speed of insights extraction is particularly slower.
MindMeld
The conversational AI platform MindMeld provides functionality for every step of today's modern conversational workflow. It assists the user in having a conversational understanding of the domain and other algorithms. This includes knowledge base creation. Blueprints are available for common conversational uses.
Features:
- The tool offers versatile command-line utilities to meet product requirements.
- It guarantees that the application retains control and ownership.
Pros:
- It is the only AI platform that supports the creation of custom knowledge bases.
- It allows users to easily discover videos using intuitive voice commands.
Cons:
- The accuracy rate is low.
- The verbal variation of language is not customized.
Stanford CoreNLP
Stanford CoreNLP is a type of backup download page that is used in Java language analysis tools. The tool enables its users to derive linguistics annotations for text. CoreNLP includes features like token and sentence boundaries, parts of speech, and numeric values. It takes the raw input of human language and analyzes the data into different sentences. Created and maintained at Stanford University, the tool currently supports eight languages and uses pipelines to produce annotations from raw text. It works on Linux, macOS, and Windows, making it very user-friendly.
Features:
- The platform enables users to derive linguistic annotations.
- The platform can identify the parts of speech in the sentence.
Pros:
- The platform can be customized by the user in any way
- It supports 8 natural languages.
Cons:
- The default command when opening the platform is slow.
- Depiction and translation of language have lots of bugs.
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Use of NLP Tools in Data Analytics
The field of data analytics has been evolving rapidly due to the advancements in tools and technologies, including machine learning and NLP. It has made it possible for organizations to have a much more comprehensive understanding of the data within documents than in the past. Let's explore some of the ways natural language processing analytics tools are being used in data analytics:
- NLP capabilities are incorporated into business intelligence and analytics products, thereby helping to enhance natural language generation for data visualization narration. This helps in making data visualizations more understandable and accessible to the audiences.
- With NLP, more employees within the organization can interact with data in a conversational manner, making the interaction more natural for non-technical team members.
- Today, NLP is changing the speed at which data is explored. Visualization software is being used to generate queries and discover answers to questions quickly.
- Surveys offer insights into a business's performance. However, when a large number of customers complete surveys, the data size increases. Organizations can use NLP to manage survey results and gather insights to perform tasks more accurately and efficiently.
- Machines can analyze larger amounts of language-based data than humans without the risk of bias or inconsistency. By incorporating automation capabilities into data analysis, data can be analyzed thoroughly.
- Natural language processing tool has applications for investigative discovery. These powerful tools can be used for spotting patterns in written reports or emails. This can be further used to detect and solve crimes.
- Sentiment analysis is a primary function of NLP. The main use involves analyzing the words in a text to establish general sentiment. This technique helps reduce results into three different areas: positive, negative, and neutral.
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Key Takeaways
- A subfield of artificial intelligence, natural language processing (NLP) examines human-computer interaction and strives to understand human speech and intentions.
- NLP is used in designing applications like word processors, search engines, chatbots, and banking apps.
- NLP is used in data analytics to enhance data visualization narration, thereby enabling non-technical team members to interact with data, speed up their data exploration processes, analyze large amounts of language-based data, and resolve language ambiguities.
Natural Language Processing Tools - Summary
In recent years, NLP has emerged as a core part of modern AI, machine learning, and other business applications. Businesses are integrating NLP capabilities into their workflows to maximize their operations, including automation, data extraction, and sentiment analysis. By understanding the specific needs and considering the above-mentioned factors, businesses can determine the perfect tool to enhance their text analysis. When choosing the right tool, remember to:
- Focus on the insights you want to gain from the text data.
- Consider user-friendliness to understand the advanced features.
- Check pricing and flexibility to fit your budget and meet growing requirements.
Investing in the best NLP software can enable businesses to streamline their processes, gain critical insights from unstructured data, and enhance customer experiences. With the right NLP tool, organizations can experience the success of their AI strategy.
A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance.
About SG Analytics
SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.
Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
