Do you think that data consumption is increasing alarmingly? We see that this data is mostly in the form of texts. NLP, full-form Natural Language Processing, is a very crucial and popular branch of Artificial Intelligence that enables Data Science professionals to extract textual data insights in an easily understandable form.
Everything we say or write has essential information and can be used to help us make wise judgments. However, since humans are capable of employing a variety of languages, words, tones, etc., retrieving this information with a machine is not that simple. In our daily lives, we generate a tonne of extremely unorganized data through our discussions. So, here lies the huge demand for Natural Language Processing professionals, such as NLP Engineers, in every industry that uses AI & ML and has to deal with large amounts of data. Interview Kickstart extends a helping hand to the aspirants or professionals in the field of NLP in Data Science,
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Here’s what we’ll cover in this article:
- What is Natural Language Processing?
- Natural Language Processing with Python: Natural Language Toolkit (NLTK)
- 11 NLP Techniques in Data Science
- NLP Case Studies: Real-Life Examples
- FAQs About Natural Language Processing in Data Science
What is Natural Language Processing?
NLP–Natural Language Processing in data science is the automatic handling of natural languages, such as speech and text. The software assists computers in observing, analyzing, recognizing, and extracting useful meaning from natural or human-spoken languages used to perform this manipulation. Through the development of techniques, this branch of data science intends to close the communication gap between data science and human languages by teaching computers to understand text conversations the same way humans do.
Computers need people to interact with them using organized, unambiguous programming languages like Java, Python, etc. So, NLP applications are challenging to construct. Professionals make computers understand spoken languages that are imprecise and adapt to regional or social changes.
Natural Language Processing with Python: Natural Language Toolkit (NLTK)
Natural language processing is done using the Python programming language. Python employs a broad spectrum of tools and libraries for carrying on specific NLP tasks. Natural Language Toolkit, or NLTK, has a collection of libraries, tools, programs, and educational resources. Such libraries are open source and help in building NLP programs.
The NLP tasks or subtasks that are carried out using libraries of the NLTK include sentence parsing, trimming words down to their roots, word segmentation, and breaking phrases, sentences, paragraphs and passages into parts for better computer understanding of the text. The Natural Language Toolkit also encompasses libraries for performing tasks like understanding the meaning in the text (semantic reasoning) and drawing logical conclusions based on information extracted from the text.
11 NLP Techniques in Data Science
What are NLP techniques? NLP techniques are systems that are used in a variety of data science applications to extract knowledge, insights, and useful information from textual data. Here are the top 11 NLP techniques discussed.
In this method, the computer breaks down content into smaller pieces, like words, phrases, or sentences. This action enables a computer to understand the content paragraph better. Here, the sentence is split, and punctuations like commas or dots are removed, making it easier for a system to deal with the text.
2. Stemming and Lemmatization
These methods further make words easier to understand. Words are reduced to their most basic form. This technique is useful when trying to decipher a text's feelings or choose the right phrases to use. It's similar to changing "moving" into "move" or "better" into "good," which makes them simpler for the computer to identify and interpret.
3. Stop Words Removal
Stop words are small regular words (like a, and, or the) that don't hold much meaning on their own in an expression. Stop word removal streamlines analysis by getting rid of common words, increasing text processing effectiveness and emphasizing the main points of the information. The removal of these words helps the system to focus easily on the main words in the text. Removing unnecessary information is similar to the removal of extra noise to help grasp the main message efficiently.
4. TF-IDF (Term Frequency-Inverse Document Frequency)
Word importance is measured by TF-IDF, which is essential for content recommendation, keyword extraction, and document retrieval systems. The technique can be used to find the keywords in a document. This is achieved by looking at two metrics, namely, the inverse document frequency of the word of concern and the term frequency of the relevant document.
A word is regarded as significant for a document if it appears frequently there but not elsewhere. This method aids in operations like document retrieval (finding the appropriate documents from a search), keyword extraction (identifying crucial words) and content recommendation (suggesting similar content).
5. Keyword Extraction
The practice of automatically locating and underlining keywords or phrases in big text collections makes it easier to summarize, tag, and identify relevant subjects. When dealing with large amounts of textual content, keyword extraction makes the process of working with text data easier, more manageable, and more clear.
6. Word Embeddings
Word embeddings are techniques for expressing words as numerical vectors that capture semantic links and improve operations like word similarity, document grouping, and language translation. Natural language processing tasks become more efficient and precise as a result of this process, which improves understanding of words and their relationships.
7. Sentiment Analysis
Sentiment analysis is a kind of tool that enables us to understand the emotions behind material that people write, such as reviews or tweets. Understanding client feedback, market mood, and social media monitoring for decision-making are all aided by analyzing the emotional tone of a text. Sentiment analysis enables us to extract emotional information from text, which may be used to inform decisions in a variety of fields, including business, finance, and marketing.
8. Topic Modeling
Using the concept of topic modeling, we can group massive document collections into topics or themes in order to make sense of them. Topic-based document organization aids in text data discovery, recommendation engines, and content categorization. It is an effective tool for classifying and interpreting massive amounts of text data, thereby making it simpler to explore, obtain pertinent material, and find hidden insights.
9. Text Summarization
Text summarization is similar to having a program that can read a lengthy article or file and then provide you with a condensed version that has all the crucial information. Concise overviews produced by summarization algorithms are crucial for reducing information, creating headlines, and assisting with information retrieval. This process of extracting important ideas from a text so that readers can quickly understand them without having to read the entire document.
10. Named Entity Recognition (NER)
The refined detective-like Named Entity Recognition (NER) program searches text for specified items like names of people, locations, dates, and more. Information extraction, content labeling, and improved search accuracy all benefit from the ability to recognize items in text (like names and locations). It is a useful technique for locating and classifying specific textual information, making it simpler to extract useful information and improving the accuracy of many text-based applications.
11. Emotion Detection
Equivalent to a detective trained to identify specific emotions like happiness, rage, or sadness rather than just whether the text is favorable or negative (as in sentiment analysis). Emotion detection takes sentiment analysis a step further by classifying text into certain emotions, which is beneficial for applications in market research and mental health. It is advantageous in a variety of industries, including marketing and mental health, because emotion detection extends beyond sentiment analysis to provide a deeper knowledge of human emotions in text.
NLP Case Studies: Real-Life Examples
Let’s look into Natural Language Processing's real-life applications through a few famous examples.
Mastercard’s Chatbot on Facebook Messenger: The chatbot analyzes client data to provide customer support services such as purchase summaries, available perks, and reminders. They were able to offer superior client service as a result. They were able to avoid incurring costs by using a chatbot instead of creating a separate customer assistance software.
Klevu–The smart search provider: Klevu is a supplier of NLP-based advanced search for e-commerce companies to improve consumer experience. It uses textual data insights to deliver individualized search recommendations, learns from customer interactions in the store, and executes features like search autocomplete.
NLP for Product Offerings: For natural language searches and data visualization narration, several business intelligence departments and analytic providers are integrating Natural Language Processing (NLP) features into their product offerings.
Uber’s Facebook Messenger Bot: For the purpose of reaching more people and gathering data, Uber deployed a messenger bot on Facebook Messenger. The bot analyzed consumer data and simplified service access, resulting in improved customer experience, increased user numbers, and enhanced company social media presence.
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FAQs About Natural Language Processing in Data Science
Q1. How to learn NLP?
You can begin your Natural Language Processing journey through online courses. Look for the best courses available online and choose the one that sits well with your requirements. Some people learn NLP on their own with the help of NLP tools. Programming skills in Python, Keras, and NumPy, as well as knowledge of manual tokenization, NLTK tokenization, and basic text data cleaning, are required to get started with NLP.
Q2. Is Natural Language Processing Machine Learning or Deep Learning?
NLP is a branch of machine learning that gives computers the ability to comprehend, interpret, and produce human language. In actuality, NLP is a branch of machine learning. Natural language processing with AI, a relation found, asin, Machine Learning is a branch of AI. Deep learning is also a subset of ML.
Q3. Does NLP require a lot of Math?
No, not much extensive knowledge of Math is required in studying NLP. The four primary areas of math and statistics must be understood in order to comprehend natural language processing algorithms. They are linear algebra, calculus, probability theory, and the fundamentals of statistics.
Q4. Is NLP high-paying?
Yes. NLP jobs can be considerably high-paying, and there are plenty of variables to consider to get the best deal for yourself. In the latest Glassdoor update of September 2023, an NLP Engineer’s average salary is $1,53,779 per year in the United States based on 54 salaries submitted anonymously. Additional cash compensation is $36,593 on average. Although more experienced individuals can make more.
Q5. Is NLP in high demand?
The job future for NLP engineers is positive, with expected employment growth, and NLP is a promising career with expanding demand in many industries. The Bureau of Labor Statistics (BLS) predicts a 23% growth in computer and information research scientist employment from 2022 to 2032, with an average of 3,400 job openings each year, primarily due to the need to replace workers who transfer or retire.