{"id":24588,"date":"2023-01-13T17:00:48","date_gmt":"2023-01-13T17:00:48","guid":{"rendered":"https:\/\/www.revuze.it\/blog\/?p=24588"},"modified":"2023-01-13T12:09:12","modified_gmt":"2023-01-13T12:09:12","slug":"natural-language-processing-techniques","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/natural-language-processing-techniques\/","title":{"rendered":"Natural Language Processing (NLP) Techniques &amp; Examples"},"content":{"rendered":"<p><strong>Data is key to understanding what customers want and need. But sifting through mountains of data and analyzing it can prove a daunting undertaking. That\u2019s where advanced AI tools come in. In this article, we\u2019ll discuss natural language processing techniques (NLP) and share examples of their application, examining how they can drive your growth.<\/strong><\/p>\n<p><span style=\"font-weight: 400\">The AI revolution is coming. Today, <\/span><a href=\"https:\/\/www.ibm.com\/downloads\/cas\/GVAGA3JP\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">35% of companies<\/span> <\/a><span style=\"font-weight: 400\">report using AI in their business, an increase of four percent from 2021. And an additional 42% report that they are exploring ways to begin using AI.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">No matter where you are in terms of readiness to begin <\/span><a href=\"https:\/\/www.revuze.it\/blog\/ai-and-market-research\/\"><span style=\"font-weight: 400\">adopting artificial intelligence<\/span><\/a><span style=\"font-weight: 400\"> and machine learning in your company, it\u2019s to your organization\u2019s benefit to learn about these emerging technologies and understand how you might be able to apply them in order to improve business outcomes.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Natural language processing, or NLP for short, is the perfect place to start.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">It\u2019s a powerful application of machine learning technology that can be used in a wide variety of industries for countless applications to help with everything from streamlining business processes to boosting efficiency to improving <\/span><a href=\"https:\/\/www.revuze.it\/blog\/ecommerce-customer-experience\/\"><span style=\"font-weight: 400\">e-commerce customer experience<\/span><\/a><span style=\"font-weight: 400\"> and brand loyalty.<\/span><\/p>\n<p><span style=\"font-weight: 400\">In this article, we\u2019ll dive into everything you need to know about natural language processing including:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">What it is.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Its advantages.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Relevant techniques.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Applications.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">And, finally, real-world examples.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Let\u2019s start from the top.<\/span><\/p>\n<h2><strong>What is natural language processing?<\/strong><\/h2>\n<p><span style=\"font-weight: 400\">Natural language processing is a branch of artificial intelligence that aims to help computers to understand human language input in the form of text or speech.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">NLP combines multiple disciplines, including computation linguistics, machine learning, deep learning, and statistics.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">These technologies work together to essentially give computer software the ability to process and understand human language in the way that another human could, including its meaning, intent, and sentiment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">NLP technology is used in a variety of applications including:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Digital assistants such as Siri.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Speech-to-text dictation software.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Voice-operated GPS systems.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Customer service chatbots.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Predictive text.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Digital voicemail.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Autocorrect.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Search autocomplete.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Email filters.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Additionally, companies are increasingly using NLP to create enterprise solutions that help businesses simplify processes, increase productivity, and streamline operations.<\/span><\/p>\n<h2><strong>The benefits of employing natural language processing<\/strong><\/h2>\n<p><span style=\"font-weight: 400\">It\u2019s standard these days for companies to collect, store, process, and analyze large quantities of numerical data in order to generate valuable insights that can improve results.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Natural language processing opens up and empowers businesses to make smarter decisions that are based on larger sets of data. Further, this collection and analysis process happens quickly, especially compared to traditional methods.<\/span><\/p>\n<p><span style=\"font-weight: 400\">For this reason, natural language processing has a number of relevant advantages.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">When working with so much data, you\u2019ll be able to generate insights to improve customer experience with the <\/span><a href=\"https:\/\/www.revuze.it\/blog\/new-product-launch\/\"><span style=\"font-weight: 400\">launch of new products<\/span><\/a><span style=\"font-weight: 400\">.<\/span><\/p>\n<p><span style=\"font-weight: 400\">On top of that, using NLP helps businesses become more efficient by automating work processes that require reviewing or analyzing texts. This frees up employees to work on other needle-moving tasks.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Taken together, you\u2019re bound to see improved productivity, reduced costs, and an uplift in revenue.<\/span><\/p>\n<h2><strong>The top techniques used in NLP<\/strong><\/h2>\n<p><span style=\"font-weight: 400\">NLP is a rich field requiring the use of a number of different techniques in order to successfully process and understand human language. Below, we review and define a selection of the techniques commonly used in NLP technology.\u00a0<\/span><\/p>\n<h3><strong>Tokenization\u00a0<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Also called word segmentation, tokenization is one of the simplest and most important techniques involved in NLP.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">It\u2019s a crucial preprocessing step in which a long string of text is broken down into smaller units called tokens. Tokens include words, characters, and subwords. They are the building blocks of natural language processing, and most NLP models process raw text on the token level.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24591\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-7.png\" alt=\"\" width=\"1050\" height=\"520\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-7.png 1050w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-7-300x149.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-7-1024x507.png 1024w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-7-768x380.png 768w\" sizes=\"(max-width: 1050px) 100vw, 1050px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">An example from <\/span><\/i><a href=\"https:\/\/medium.com\/@ajay_khanna\/tokenization-techniques-in-natural-language-processing-67bb22088c75\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">Medium<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> of how a simple phrase can be broken down into tokens.<\/span><\/i><\/p>\n<h3><strong>Stemming &amp; lemmatization<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">After tokenization, the next preprocessing step is either stemming or lemmatization. These techniques generate the root word from the different existing variations of a word.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">For example, the root word \u201cstick\u201d can be written in many different variations, like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Stick<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Stuck<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Sticker<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Sticking\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Sticks<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Unstick<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Stemming and lemmatization are two different ways to try to identify a root word. Stemming works by removing the end of a word. This NLP\u00a0 technique may or may not work depending on the word. For example, it would work on \u201csticks,\u201d but not \u201cunstick\u201d or \u201cstuck.\u201d\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Lemmatization is a more sophisticated technique that uses morphological analysis to find the base form of a word, also called a lemma.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24592\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-6.png\" alt=\"\" width=\"1025\" height=\"569\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-6.png 1025w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-6-300x167.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-6-768x426.png 768w\" sizes=\"(max-width: 1025px) 100vw, 1025px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">The difference between how stemming and lemmatization work is illustrated in this image from <\/span><\/i><a href=\"https:\/\/itnext.io\/introduction-to-natural-language-processing-nlp-tools-for-python-cf39af3cfc64?gi=e7ee4400b07d\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">itnext<\/span><\/i><\/a><i><span style=\"font-weight: 400\">, using different forms of the word \u201cchange.\u201d<\/span><\/i><\/p>\n<h3><strong>Morphological segmentation<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Morphological segmentation is the process of splitting words into the morphemes that make them up. A <\/span><a href=\"https:\/\/www.sciencedirect.com\/topics\/psychology\/morpheme\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">morpheme<\/span><\/a><span style=\"font-weight: 400\"> is the smallest unit of language that carries meaning. Some words such as \u201ctable\u201d and \u201clamp\u201d only contain one morpheme.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">But other words can contain multiple morphemes. For example, the word \u201csunrise\u201d contains two morphemes: sun and rise. Like stemming and lemmatization, morphological segmentation can help preprocess input text.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24590\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-8.png\" alt=\"\" width=\"1024\" height=\"768\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-8.png 1024w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-8-300x225.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-8-768x576.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">John Hopkins shows morphological segmentation by breaking the word \u201cunachievability\u201d into its morphemes.<\/span><\/i><\/p>\n<h3><strong>Stop words removal<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Stop words removal is another preprocessing step of NLP that removes filler words to allow the AI to focus on words that hold meaning. This includes conjunctions such as \u201cand\u201d and \u201cbecause,\u201d as well as prepositions such as \u201cunder\u201d and \u201cin.\u201d\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">By removing these unhelpful words, NLP systems are left with less data to process, allowing them to work more efficiently. It isn\u2019t a necessary step of every NLP use case, but it can help with things such as text classification.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24595\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-3.png\" alt=\"\" width=\"546\" height=\"170\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-3.png 546w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-3-300x93.png 300w\" sizes=\"(max-width: 546px) 100vw, 546px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">Examples from <\/span><\/i><a href=\"https:\/\/www.geeksforgeeks.org\/removing-stop-words-nltk-python\/\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">geeksforgeeks<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> of what short phrases look like with the stop words removed.<\/span><\/i><\/p>\n<h3><strong>Text classification<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Text classification is an umbrella term for any technique used to organize large quantities of raw text data. Sentiment analysis, topic modeling, and keyword extraction are all different types of text classification. And we\u2019ll talk about them shortly.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Text classification essentially takes unstructured text data and structures it, preparing it for further analysis. It can be used on nearly every text type and help with a number of different organization and categorization applications.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">In this way, text classification is an essential part of natural language processing, used to help with everything from detecting spam to monitoring brand sentiment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Some possible applications of text classification include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Grouping product reviews into categories based on sentiment.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Flagging customer emails as more or less urgent.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Organizing content by topic.<\/span><\/li>\n<\/ul>\n<h3><strong>Sentiment analysis<\/strong><\/h3>\n<p><a href=\"https:\/\/www.revuze.it\/blog\/sentiment-analysis\/\"><span style=\"font-weight: 400\">Sentiment analysis<\/span><\/a><span style=\"font-weight: 400\">, also known as emotion AI or opinion mining, is the process of analyzing text to determine whether it is generally positive, negative, or neutral.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">As one of the most important NLP techniques for text classification, sentiment analysis is commonly used for applications such as analyzing user-generated content. It can be used on a variety of text types, including reviews, comments, tweets, and articles.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The Revuze platform employs sentiment analysis to understand how customers feel about various aspects of products. This allows companies to gain insights about consumers\u2019 needs in real-time, and act accordingly to improve overall CX.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24594\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-4.png\" alt=\"\" width=\"1269\" height=\"854\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-4.png 1269w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-4-300x202.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-4-1024x689.png 1024w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-4-768x517.png 768w\" sizes=\"(max-width: 1269px) 100vw, 1269px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">In this example from the Revuze platform, you can see how customers rate different aspects of the product.<\/span><\/i><\/p>\n<h3><strong>Topic modeling<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Topic modeling is a technique that scans documents to find themes and patterns within them, clustering related expressions and word groupings as a way to tag the set.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">It\u2019s an unsupervised machine learning process, meaning that it doesn\u2019t require the documents it is processing to have previously been categorized by humans.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24593\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-5.png\" alt=\"\" width=\"810\" height=\"292\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-5.png 810w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-5-300x108.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-5-768x277.png 768w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">A sample NLP workflow from <\/span><\/i><a href=\"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2020.00042\/full\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">Frontiersin<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> demonstrates how Input text is proprocessed before undergoing topic modeling, which breaks it into several topics.\u00a0<\/span><\/i><\/p>\n<h3><strong>Keyword extraction<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Keyword extraction is a technique that skims a document, ignoring the filler words and honing in on the important keywords. It is used to automatically extract the most frequently used and essential words and phrases from a document, helping to summarize it and identify what it\u2019s about.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">This is highly useful for any situation in which you want to identify a topic of interest in a textual dataset, such as whether there is a problem that comes up again and again in customer emails.\u00a0<\/span><\/p>\n<h3><strong>Text summarization<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">This NLP technique summarizes a text in a coherent way, and it\u2019s great for extracting useful information from a source. While a human would have to read an entire document in order to write an accurate summary of it, which takes quite a bit of time, automatic text summarization can do it much more quickly.<\/span><\/p>\n<p><span style=\"font-weight: 400\">There are two types of text summarization:<\/span><b><\/b><\/p>\n<ul>\n<li><b>Extraction-based<\/b><span style=\"font-weight: 400\"> &#8211; This technique pulls key phrases and words from the document to make a summary without changing the original text.<\/span><\/li>\n<\/ul>\n<ul>\n<li><b>Abstraction-based<\/b><span style=\"font-weight: 400\"> &#8211; This technique creates new phrases and sentences based on the original document, essentially paraphrasing it.<\/span><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24596\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-2.png\" alt=\"\" width=\"1046\" height=\"432\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-2.png 1046w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-2-300x124.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-2-1024x423.png 1024w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-2-768x317.png 768w\" sizes=\"(max-width: 1046px) 100vw, 1046px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">An example from the <\/span><\/i><a href=\"https:\/\/techcommunity.microsoft.com\/t5\/ai-customer-engineering-team\/bootstrap-your-text-summarization-solution-with-the-latest\/ba-p\/1268809\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">Microsoft tech community<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> of how the two types of text summarization work.<\/span><\/i><\/p>\n<h3><strong>Parsing<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Parsing is the process of figuring out the grammatical structure of a sentence, determining which words belong together as phrases and which are the subject or object of a verb. This NLP technique offers additional context about a text in order to help with processing and analyzing it accurately.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24597\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-1.png\" alt=\"\" width=\"1280\" height=\"720\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-1.png 1280w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-1-300x169.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-1-1024x576.png 1024w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-1-768x432.png 768w\" sizes=\"(max-width: 1280px) 100vw, 1280px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">This is how <\/span><\/i><a href=\"https:\/\/www.youtube.com\/watch?v=yHGndPD0Xs0&amp;ab_channel=Lastmomenttuitions\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">parsing<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> might work on a short sentence.<\/span><\/i><\/p>\n<h3><strong>Named entity recognition<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Named entity recognition (NER) is a type of information extraction that locates and tags \u201cnamed entities\u201d with predefined keywords such as names, locations, dates, events, and more.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">In addition to tagging a document with keywords, NER also keeps track of how many times a named entity is mentioned in a given dataset. NER is similar to keyword extraction, but the extracted keywords are put into predefined categories.<\/span><\/p>\n<p><span style=\"font-weight: 400\">NER can be used to identify how often a certain term or topic is mentioned in a given data set. For example, it might be used to identify that a certain issue, tagged as a word like \u201cslow\u201d or \u201cexpensive,\u201d comes up again and again in customer reviews.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-24598\" src=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed.png\" alt=\"\" width=\"1000\" height=\"600\" srcset=\"https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed.png 1000w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-300x180.png 300w, https:\/\/www.revuze.it\/blog\/wp-content\/uploads\/sites\/2\/2023\/01\/unnamed-768x461.png 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><i><span style=\"font-weight: 400\">A sample by <\/span><\/i><a href=\"https:\/\/www.shaip.com\/blog\/named-entity-recognition-and-its-types\/\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">Shaip<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> of how named entity recognition works.\u00a0<\/span><\/i><\/p>\n<h3><strong>TF-IDF<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">TD-IDF, which stands for term frequency-inverse document frequency, is a statistical technique that determines the relevance of a word to one document in a collection of documents. It works by looking at two metrics: the number of times a word appears in a given document and the number of times the same word appears in a set of documents.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">If a word is common in every document, it won\u2019t receive a high score, even if it appears many times. But if a word frequently repeats in one document while rarely appearing in the rest of the documents in a set, it will rank high, suggesting it is highly relevant to that one document in particular.\u00a0<\/span><\/p>\n<h2><strong>Natural language processing applications\u00a0<\/strong><\/h2>\n<p><span style=\"font-weight: 400\">NLP is a quickly developing technology with many different applications for organizations of every kind. Some of the different ways a business can benefit from NLP include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><b>Machine translation<\/b><span style=\"font-weight: 400\"> &#8211; Using NLP, computers can translate large amounts of text from a target to a source language, which can be used for customer support, data mining, and even publishing multilingual content.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Information retrieval<\/b><span style=\"font-weight: 400\"> &#8211; NLP can be used to quickly access and retrieve information based on a user\u2019s query from text repositories such as file servers, databases, and the internet.<\/span><\/li>\n<li style=\"font-weight: 400\"><strong>Sentiment analysis<\/strong> &#8211; This NLP technique can be used to monitor brand and product sentiment to help with customer service and <a href=\"https:\/\/www.revuze.it\/blog\/what-is-product-innovation\/\"><span style=\"text-decoration: underline\">product sentiment<\/span><\/a>, among other applications.<\/li>\n<li style=\"font-weight: 400\"><b>Information extracting<\/b><span style=\"font-weight: 400\"> &#8211; This process, which includes retrieving information from unstructured data and extracting it into structured, editable formats, can be used for business intelligence, including <a href=\"https:\/\/www.revuze.it\/blog\/competitive-intelligence-2\/\"><span style=\"text-decoration: underline\">competitive intelligence<\/span><\/a>.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Question answering <\/b><span style=\"font-weight: 400\">&#8211; Question answering uses NLP to give an answer to a question asked in natural human language and can be used for chatbots and customer support.<\/span><\/li>\n<\/ul>\n<h2><strong>Natural language processing examples<\/strong><\/h2>\n<p><span style=\"font-weight: 400\">Here are just a few more concrete examples of ways an organization might apply NLP to its business processes.<\/span><\/p>\n<h3><strong>NLP in ChatGPT<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">One of the most popular recent applications of NLP technology is ChatGPT, the trending AI chatbot that\u2019s probably all over your social media feeds. ChatGPT is fueled by NLP technology, using a multi-layer transformer network to generate human-like written responses to inquiries submitted in natural human language. ChatGPT uses unsupervised learning, which means it can generate responses without being told what the correct answer is.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">ChatGPT is an exciting step forward in the application of NLP technology for businesses and individuals alike, with <\/span><a href=\"https:\/\/www.cnbc.com\/2022\/12\/15\/google-vs-chatgpt-what-happened-when-i-swapped-services-for-a-day.html\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">many saying<\/span><\/a><span style=\"font-weight: 400\"> it can rival even Google. Possible uses for ChatGPT include customer service, translation, summarization, and even content writing.\u00a0<\/span><\/p>\n<h3><strong>NLP for customer experience analytics<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Using NLP for <\/span><a href=\"https:\/\/www.revuze.it\/blog\/social-media-listening-definition\/\"><span style=\"font-weight: 400\">social listening<\/span><\/a><span style=\"font-weight: 400\"> and customer review analysis can lead to tremendous insight into what customers are thinking and saying about a brand and its products. With sentiment analysis and text classification, companies can:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Understand general sentiment about the brand &#8211; Does the public feel positively or negatively about us?\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Identify what customers like and dislike about a service or product.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Learn what new products customers might be interested in.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Know which products to scale and which to pull back on.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Discover insights that can be used to improve customer experience and boost customer satisfaction.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">For example, let\u2019s say spicy chocolate brand Shock-O just released a new Popping Jalapeno Chocolate and wants to get a sense of whether or not customers like it. Shock-O can use an NLP-powered tool to analyze customer sentiment and learn what people are saying about the Popping Jalapeno Chocolate, whether they speak about it positively or negatively, and what themes come up again and again in reviews of this product.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">All of this information can then be used to determine whether to continue producing Popping Jalapeno Chocolate, whether to increase or decrease its production of it, whether to make it spicier or less spicy, etc.\u00a0<\/span><\/p>\n<h3><strong>NLP for customer service<\/strong><\/h3>\n<p><a href=\"https:\/\/blog.hubspot.com\/news-trends\/live-chat-go-to-market-flaw\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">90% of customers<\/span><\/a><span style=\"font-weight: 400\"> believe that it is essential or very important to receive an immediate response when they have a question. Yet human customer service representatives are limited in availability and bandwidth.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">This is just one reason why NLP-powered chatbots are growing in popularity. By being able to properly understand and analyze customer inquiries, chatbots can offer the necessary answers to questions, helping to improve customer satisfaction while cutting down on agents\u2019 workload.<\/span><\/p>\n<p><span style=\"font-weight: 400\">NLP can also be used to process and analyze customer service surveys and tickets in order to better understand what issues customers are having, what they\u2019re happy with, what they\u2019re unhappy with and more. All of this serves as crucial data for boosting customer happiness, which will, in turn, increase customer retention and improve word-of-mouth.<\/span><\/p>\n<h3><strong>NLP for recruitment<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">HR professionals spend countless hours reviewing resumes in order to identify suitable candidates. NLP can make this process much more efficient by taking over the screening process and analyzing resumes for certain keywords.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">For example, you might set up an NLP system to flag any resume that uses the word \u201cPython\u201d or \u201cleadership\u201d for a human to review later on.<\/span><\/p>\n<p><span style=\"font-weight: 400\">This can increase the likelihood of finding strong candidates, helping an organization fill open positions more quickly and with better talent. What\u2019s more, it can also free up HR professionals\u2019 time to focus on tasks that require more strategic thinking.<\/span><\/p>\n<h2><strong>Conclusion<\/strong><\/h2>\n<p><span style=\"font-weight: 400\">The idea that data has important insights to offer companies has been widely accepted, leading businesses to invest in various business intelligence technologies in order to improve their processes and offerings.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">But if your organization is only mining numerical data, you\u2019re missing out on a wealth of valuable information to be found in unstructured human language-based data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Natural language processing is a powerful technology allowing text and words to be analyzed as efficiently as numbers can. By learning about and investing in NLP, you\u2019ll be able to achieve a number of desirable outcomes, including streamlining processes, improving brand reputation and loyalty, and ultimately boosting revenue.<\/span><\/p>\n<p><span style=\"font-weight: 400\">The next step would be taking these actionable insights and using them to further drive CX with <\/span><a href=\"https:\/\/www.revuze.it\/blog\/e-commerce-personalization\/\"><span style=\"font-weight: 400\">e-commerce personalization<\/span><\/a><span style=\"font-weight: 400\">.<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data is key to understanding what customers want and need. But sifting through mountains of data and analyzing it can prove a daunting undertaking. That\u2019s where<\/p>\n","protected":false},"author":29,"featured_media":24589,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[8,23,60,59,45,51],"acf":[],"_links":{"self":[{"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/posts\/24588"}],"collection":[{"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/users\/29"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/comments?post=24588"}],"version-history":[{"count":0,"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/posts\/24588\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/media\/24589"}],"wp:attachment":[{"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/media?parent=24588"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/categories?post=24588"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revuze.it\/blog\/wp-json\/wp\/v2\/tags?post=24588"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}