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Natural Language Processing (NLP) in Data Science: 10 Techniques

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14th Sep, 2023
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    Natural Language Processing (NLP) in Data Science: 10 Techniques

    In this rapidly developing world, data consumption is increasing at an alarming rate. A large portion of this data is in the form of text. NLP full form in data science is Natural Language Processing, and it is a popular branch of AI that helps Data Scientist extract insights from textual data. This has brought a huge demand for Natural Language Processing professionals. Everything we speak or express holds great information and can be useful in making valuable decisions. But extracting this information using a machine is not that easy as humans can use multiple languages, words, tones, etc. All the data we generate through our conversations in our day-to-day life is highly unstructured. But thanks to advanced data science and natural language processing techniques, machines can now have meaningful conversations with human beings. In this article, we will discuss and dive deep into the ten most used NLP Techniques in Data Science. 

    What is Natural Language Processing (NLP)?

    Natural Language Processing or NLP in data science is the automatic manipulation of natural languages, like speech and text, by using software that helps computers observe, analyze, understand, and derive valuable meaning from natural or human-spoken languages. In other words, it is a branch of data science that focuses on training computers to process and interpret conversations in text format in a way humans do by listening. It is a field that is developing methodologies for filling the gap between Data Science and human languages. NLP applications are difficult and challenging during development as computers require humans to interact with them using programming languages like Java, Python, etc., which are structured and unambiguous. But human-spoken languages are ambiguous and change with regional or social change, so it becomes challenging to train computers to understand natural languages. So irrespective of the location the best Data Science Certification program remains incomplete without a live project on NLP. 

    Let us now dive deep and understand the ten most used NLP Techniques in Data Science. 

    10 NLP Techniques in Data Science

    1. Tokenization in NLP

    Tokenization is one of the NLP techniques that segments the entire text into sentences and words. In other words, we can say that it is a process of dividing the text into segments called tokens. This process discards certain characters like punctuation, hyphens, etc. The main purpose of tokenization is to convert the text into a format that is more convenient for analysis. 

    Let us understand this with the help of an example.

    In this case, it was quite simple as we split and classified it into blank spaces. The problem with tokenization is the removal of punctuation. Sometimes it may lead to complications. For example, in Mr., the period following the abbreviation should be a part of the same token and should not be removed, but tokenization splits it into two words. Because of this, a large number of problems arise while applying tokenization to biomedical text domains having a number of hyphens, parentheses, and punctuations. 

    2. Stemming and Lemmatization

    The main objective of Stemming in NLP is to reduce the words to their root form. The stemming technique works on the principle that certain kind of words having slightly different spellings but having the same meaning should be placed in the same token. In stemming, the affixes are removed for efficient processing. 

    In Lemmatization, we convert the words into lemma which is the dictionary form of the word. For example, “Hates”, and “hating” are forms of the word “hate”. So “hate” will be the lemma for these words. The Lemmatization technique aims at converting the different forms of a word to their root form and grouping them together. The aim of stemming and lemmatization is quite similar, but the approaches are different. 

    Let us understand both approaches with an example.

    3. Stop Words Removal

    In Stop Words Removal technique, the common words which occur most frequently but add very little or no value to the result are automatically removed from the text. This helps to free up space and improve performance and processing time. The main purpose of using this technique is to minimize the noise so that we can focus on the words holding important meaning during the analysis. For example, the common prepositions like and, the, a, of the English language can be removed. This technique is not much preferred in analysis as sometimes some important information is lost in this method. Master stop words removal and other techniques used in NLP for data science with our online Data Science Bootcamp. 

    4. Term Frequency-Inverse Document Frequency (TF-IDF)

    TF or Term frequency measures the frequency of a word in a given document. This is calculated by counting the total number of occurrences of the word and dividing it by the total length of the document i.e - TF=Total occurrences/Total length of the document. 

    IDF or Inverse Document Frequency assigns a weight to any string according to its importance. It calculates it by taking the log of the total number of documents in the dataset present at that time divided by the number of documents containing that particular word. TF-IDF is the importance of any word by multiplying the TF and IDF terms i.e TF*IDF. 

    Thus, by this method, the words having more importance are assigned higher weights by using these statistics. TF-IDF technique is mostly used by search engines for scoring and ranking the relevance of any document according to the given input keywords. 

    5. Keyword Extraction in NLP

    Keyword extraction is a text analysis technique that automatically extracts the most used and most important words and expressions from a given text. It helps summarize the content of texts and recognize the main topics discussed. 

    It finds keywords from all texts i.e- regular documents and business reports, tweets, social media comments, online forums and reviews, news reports, and many more. By using the Keyword Extraction technique, we can automatically see what our customers are mentioning most often on the internet, saving the teams hours upon hours of manual processing using traditional methods. 

    As more than 80% of the data generated every day is unstructured, making it extremely difficult to analyze and process – businesses need automated keyword extraction to help them process and analyze customer data in a more efficient manner. 

    6. Word Embeddings

    Word Embeddings in NLP is a technique of representing the words of a document in the form of numbers. It should be represented in a way that similar words have a similar representation. It is a technique where individual words of a domain or language are represented as real-valued vectors in a lower dimensional space. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems. Each word is represented by a real-valued vector, often tens or hundreds of dimensions. 

    7. Sentiment Analysis

    Sentiment Analysis is a machine learning and natural language processing (NLP) technique used to examine the emotional tone conveyed by the user in any piece of text or sentence. It is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinions and acting accordingly. The emotional tone or the feedback here could be positive, negative, or neutral. 

    Businesses use sentiment analysis tools such as to assess the sentiment value of their brands, goods, services, and even customer feedback. Customers’ emotions/sentiments can be analyzed and evaluated using sentiment analysis software. 

    There are a total of 5 types of Sentiment Analysis techniques used in NLP: 

    1. Emotion Detection Sentiment Analysis 
    2. Aspect-Based Sentiment Analysis 
    3. Fine-Grained Sentiment Analysis 
    4. Multilingual Sentiment Analysis 
    5. Intent Sentiment Analysis 

    8. Topic modeling

    Topic Modeling is a technique in NLP that extracts important topics from the given text or document. It works on the assumption that each document is a group of topics, and each topic is a group of words. We can relate it with Dimensionality Reduction. 

    Firstly, the user defines the number of topics a document should have. The algorithm will then divide the document into topics in such a way that the topics should include all the words in the document. The algorithm then iteratively assigns the words to any topic based on its probability of belonging to that topic and the probability that it can regenerate the document from those topics. This is useful because extracting the words from a document takes more time and is much more complex than extracting them from topics present in the document. 

    9. Text Summarization

    Text summarization is a very useful and important part of Natural Language Processing (NLP). It is used to build algorithms or programs which will reduce the text size and create a summary of our text data. This is called automatic text summarization in machine learning. Text summarization takes an input of a sequence of words i.e- the input article, and returns an output of words i.e- the summary. Such models are called sequence-to-sequence models. Text summarization can be a useful case study in domains like financial research, question-answer bots, media monitoring, social media marketing, and so on. 

    10. Named Entity Recognition

    The named entity recognition technique in NLP is the task of identifying and categorizing key information (entities) in text. An entity can be any word or series of words that consistently refers to the same thing. Every detected entity is classified into a predetermined category. The Named Entity Recognition API works behind the scenes to identify and spot the relevant entities in this search. This speeds up the search process as all the relevant tags are stored together and highlighted. 

    The Named Entity Recognition technique is a two-step process: 

    1. Detect a named entity 
    2. Categorize the entity 

    Real-Life NLP Case Studies

    • Many e-commerce businesses are using Klevu, a smart search provider based on NLP to provide a better customer experience. This smart search provider automatically learns from the user interactions in the store. It performs many functions like search autocomplete, the addition of relevant contextual synonyms, etc. It also uses the insights gained from the textual data to provide personalized search recommendations. 
    • Mastercard launched its Chatbot on Facebook Messenger Application. The aim of this chat-bot was to provide customer support services like an overview of their spending habits, available benefits, and reminders by analyzing their data. This helped them to provide a better customer experience. This initiative of chat-bot resulted in saving their expenses of developing a separate app for customer support. 
    • Recently, many business intelligence units and analytic vendors have started to add NLP capabilities to their product offerings. Natural language understanding and Natural language generation are being used for natural language searches and data visualization narration, respectively. 
    • Uber also launched its messenger bot on Facebook Messenger Application. The aim was to reach more and more customers to collect more data, and Facebook was the best possible way to connect people through social media. This bot helped them in providing a better and more personalized customer experience by analyzing the customer data. This bot provided the users with easy and quick access to the service, which eventually helped them in gaining more users. 

    Conclusion

    Natural Language Processing plays a very important role in the improvisation of machine-human interactions. In this article, we have explored many aspects related to NLP, such as its definition, its methods, how it works, real-life case study, etc. We have also seen how different companies are using NLP and data science to improve their business. If you are interested in interacting with computing systems and have programming and linguistic knowledge, learning natural language processing is valuable.  

    Due to an increase in data and the need to interact with computers, the need for natural language processing is increasing day by day, and various job opportunities are coming into the market. Therefore, there is a great scope for NLP in the future. Application of natural language processing, data science, ML, and AI has changed the way we interact with computers, and it will continue to do so in the future. These AI technologies will power the transformation from data-driven to intelligence-driven initiatives while shaping and improving communication technology in the years to come. I hope this article will help you to have a clear understanding of Natural Language Processing. Sharpen Your Skills with the best Data Science Certification at KnowledgeHut. 

    Frequently Asked Questions (FAQs)

    1Is NLP Data Science or Artificial Intelligence?

    Natural Language Processing, or NLP, is a field of Artificial Intelligence that gives machines the ability to read, understand and derive meaning from human languages.

    2Is NLP related to Data Science?

    NLP is considered as the specific use-case/problem from the general focus, which is machine learning and which in turn, is an integral part of Data Science. 

    3Why is NLP the future?

    With the increasing amount of text data being generated every day, NLP will only become more and more important in the future to make sense of the data and use it in many other applications.

    4Why is NLP difficult?

    Considering how challenging human language is and how differently each individual uses it to communicate, NLP has a long way to go to become fluid, consistent, and robust.

    Profile

    Eshaan Pandey

    Author

    Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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