By Corey Ginsberg
What is Natural Language Processing?Natural Language Processing (NLP) is a subfield of artificial intelligence that studies the interaction between computers and languages. The goals of NLP are to find new methods of communication between humans and computers and grasp human speech as it is uttered. This technology combines machine learning with computational linguistics, statistics, and deep learning models so that computers can process human language from voice or text data and grasp its entire meaning and the writer or speaker’s intentions.
NLP is often used for developing word processor applications and translation software. In addition, search engines, banking apps, translation software, and chatbots rely on NLP to better understand how humans speak and write.
Uses of Natural Language Processing in Data AnalyticsThe field of data analytics has been rapidly evolving in the past years, in part thanks to the advancements with tools and technologies like machine learning and NLP. It’s now possible to have a much more comprehensive understanding of the information within documents than in the past.
Here are just a few of the ways NLP is currently being used in data analytics:
NLP capabilities are being incorporated into business intelligence and analytics products, which can enhance natural language generation for data visualization narration. By doing so, data visualizations are more understandable and accessible to various audiences. The act of narrating data visualizations not only creates a more effective storytelling experience but also makes it less likely that the data will be interpreted subjectively.
Thanks to NLP, more people within a given organization (besides data analysts and data scientists) are now able to interact with data. Because data can be approached in a conversational manner, this interaction is more natural for non-technical team members and still offers the same important insights about the data.
NLP is changing the speed at which data can be explored. Visualization software can now generate queries and find answers to questions as quickly as these questions can be uttered or typed.
Surveys can provide helpful insights into how a company is performing. However, when a large number of customers complete surveys, the data size also increases. At that point, it’s no longer possible for one person to read the results and formulate a conclusion. Companies that use NLP to manage survey results and gather insights are able to do so much more accurately and efficiently than a human would be able to.
Machines are able to analyze a much larger amount of language-based data than a human can, without the risk of bias, inconsistency, or fatigue. By incorporating automation capabilities into data analysis, text and speech data can be quickly and thoroughly analyzed.
The ability to understand human language is no easy task. People have many manners of verbal and written expression. In addition to the hundreds of languages and dialects currently being used, there are nuances to each of these, such as grammar and syntax rules, regional accents, and slang expressions. NLP can resolve language ambiguities and provide a helpful numeric structure to the data, which aids with textual analytics and speech recognition.
NLP has applications for investigative discovery. It is a powerful tool for spotting patterns in written reports or emails, which can be used not just to detect but also to solve crimes.
Text mining is a type of AI that incorporates NLP to convert unstructured text within documents or databases into structured data that can then be analyzed or used for machine learning algorithms. Once the data is structured, it can also be incorporated into data warehouses, databases, or dashboards, at which time it can be used for various types of analytical analyses, such as predictive, prescriptive, and descriptive.
By incorporating keyword extraction algorithms to reduce a large body of text into several ideas and keywords, it’s possible to glean the main topic of the text without having to read the document.
When working with a text, text statistics visualizations can offer valuable insights about sentence length, word frequency, and worth length, and display this information in histograms or bar charts.
Sentiment analysis is one of the primary functions of NLP. The main use of sentiment analysis is to analyze the words in a text so that the general sentiment of the text can be established. This technique is able to reduce results into three areas: positive, negative, and neutral. Results that offer a negative number indicate that the text has a negative tone; those with a corresponding positive number indicate positive sentiments in the text.
8 Best Tools for Natural Language Processing in 2021The following list highlights eight of the best tools and platforms for Data Analysts and Data Scientists to use for Natural Language Processing in 2021:
1. Gensim is a high-speed, scalable Python library that focuses primarily on topic modeling tasks. It excels at recognizing the similarities between texts, as well as navigating various documents and indexing texts. One of the main benefits of using Gensim is that it can handle huge data volumes.
2. SpaCy is one of the newer open-source NLP processing libraries. This Python library performs quickly and is well-documented. It is able to handle large datasets and provides users with a plethora of pre-trained NLP models. SpaCy is geared toward those who are getting text ready for deep learning or extraction.
3. IBM Watson offers users a range of AI-based services, each of which is stored in the IBM cloud. This versatile suite is well-suited to perform Natural Language Understanding tasks, such as identifying keywords, emotions, and categories. IBM Watson’s versatility lends itself to use in a range of industries, such as finance and healthcare.
4. Natural Language Toolkit (NLTK) enables users to create Python programs that are compatible with human language data. NLTK has user-friendly interfaces to more than 50 lexical and corpora resources, as well as various text processing libraries and a robust discussion forum. This free, open-source platform is commonly used by educators, students, linguists, engineers, and researchers.
5. MonkeyLearn is an NLP-powered platform that provides users with a means for gathering insights from text data. This user-friendly platform offers pre-trained models that can perform topic classification, keyword extraction, and sentiment analysis, as well as customized machine learning models that can be changed to meet various business needs. MonkeyLearn can also connect to apps like Excel or Google Sheets to perform text analysis.
6. TextBlob is a Python library that functions as an extension of NLTK. When using this intuitive interface, beginners can easily perform tasks like part-of-speech tagging, text classification, and sentiment analysis. This library tends to be more accessible to those who are new to NLP than other libraries.
7. Stanford Core NLP was created and is currently being maintained by those at Stanford University who are working on NLP. This Java library requires users to install the Java Development Kit onto their computer. It offers APIs in almost every programming language and is well-suited for executing tasks such as tokenization, named entity recognition, and part-of-speech tagging. Because Core NLP provides scalability and speed optimization, it works well for performing complicated tasks.
8. Google Cloud Natural Language API is part of Google Cloud. It incorporates question-answering technology, as well as language understanding technology. This interface offers users a variety of pre-trained models that can be used for performing entity extraction, content classification, and sentiment analysis.
The field of data analytics is being transformed by natural language processing capabilities. In the coming years, as technology continues to change and inform how humans interact with computers, as well as how computers handle big data, the field of data analytics is expected to continue to evolve in new and exciting ways with the help of newly developed tools and platforms.
Hands-On Data Analytics & Machine Learning ClassesAre you interested in learning more about the field of data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.
In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $60,229.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, natural language processing, data visualization, data science, and machine learning, among others.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$60,229. For prospective students looking for classes that teach natural language processing or machine learning, Noble’s Machine Learning Classes Near Me tool can be used to search through more than a dozen options by top providers.
Source: Noble Desktop
Original Content:
https://shorturl.at/zKL58
