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class="text-center text-nebula"></h5> <h1 class="text-zinc-100 text-center text-32 lg:text-heading-xl"> The 7 Basic Functions of Text Analytics & Text Mining </h1> </header> </div> <div class="entry-content relative py-60 bg-white max-w-3xl w-full mx-auto"> <div class="relative px-4 sm:px-6 lg:px-8"> <div> <p>Text analytics and natural language processing (NLP) are often portrayed as ultra-complex computer science functions that can only be understood by trained data scientists. But the core concepts are pretty easy to understand even if the actual technology is quite complicated. In this article I’ll review the basic functions of text analytics and explore how each contributes to deeper natural language processing features.</p> <p>Quick background: text analytics (also known as text mining) refers to a discipline of computer science that <a href="https://www.lexalytics.com/resources/ml-nlp-whitepaper/" target="_blank" rel="noopener noreferrer">combines machine learning and natural language processing (NLP)</a> to draw meaning from unstructured text documents. Text mining is how a business analyst turns 50,000 hotel guest reviews into <a href="https://www.lexalytics.com/blog/customer-feedback-analytics-guide-hospitality/">specific recommendations</a>; how a workforce analyst <a href="https://www.lexalytics.com/blog/voice-of-employee-analytics-guide/">improves productivity and reduces employee turnover</a>; how healthcare providers and biopharma researchers <a href="https://www.lexalytics.com/blog/text-analytics-nlp-healthcare-applications/">understand patient experiences</a>; and <a href="https://www.lexalytics.com/blog/nlp-applications-business/">much, much more</a>.</p> <p>Okay, now let’s get down and dirty with how text analytics really works.</p> <h2>The 7 Basic Functions of Text Analytics</h2> <p>Much like a student writing an essay on <em>Hamlet</em>, a text analytics engine must break down sentences and phrases before it can actually analyze anything. Tearing apart <a href="https://en.wikipedia.org/wiki/Unstructured_data" target="_blank" rel="noopener">unstructured text documents</a> into their component parts is the first step in pretty much every <a href="https://www.lexalytics.com/technology/features">NLP feature</a>, including <a href="https://www.lexalytics.com/technology/entity-extraction/">named entity recognition</a>, <a href="https://www.lexalytics.com/technology/text-analytics/">theme extraction</a>, and <a href="https://www.lexalytics.com/technology/sentiment-analysis/">sentiment analysis</a>.</p> <p>There are 7 basic steps involved in preparing an unstructured text document for deeper analysis:</p> <ol> <li>Language Identification</li> <li>Tokenization</li> <li>Sentence Breaking</li> <li>Part of Speech Tagging</li> <li>Chunking</li> <li>Syntax Parsing</li> <li>Sentence Chaining</li> </ol> <p>Each step is achieved on a spectrum between pure machine learning and pure software rules. Let’s review each step in order, and discuss the contributions of machine learning and rules-based NLP.</p> <h2>1. Language Identification</h2> <p><figure id="attachment_2569" aria-describedby="caption-attachment-2569" style="width: 1110px" class="wp-caption alignnone"><img fetchpriority="high" decoding="async" class="wp-image-2569 size-full" src="https://www.lexalytics.com/wp-content/uploads/2022/05/1.png" alt="" width="1110" height="838" srcset="https://www.lexalytics.com/wp-content/uploads/2022/05/1.png 1110w, https://www.lexalytics.com/wp-content/uploads/2022/05/1-300x226.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/05/1-1024x773.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/05/1-768x580.png 768w" sizes="(max-width: 1110px) 100vw, 1110px" /><figcaption id="caption-attachment-2569" class="wp-caption-text"><em>Fig. 1 – Lexalytics’ (an InMoment company) text analytics technology and NLP feature stack, showing the layers of processing each text document goes through to be transformed into structured data.</em></figcaption></figure></p> <p>The first step in text analytics is identifying what language the text is written in. Spanish? Singlish? Arabic? Each language has its own idiosyncrasies, so it’s important to know what we’re dealing with.</p> <p>As basic as it might seem, <strong>language identification</strong> determines the whole process for every other text analytics function. So it’s <em>very </em>important to get this sub-function right.</p> <p>Lexalytics supports <a href="https://www.lexalytics.com/technology/features">29 languages</a> (first and final shameless plug) spanning dozens of alphabets, <a href="https://en.wikipedia.org/wiki/Abjad#:~:text=An%20abjad%20(%2F%CB%88%C3%A6b,Daniels." target="_blank" rel="noopener">abjads</a> and <a href="https://en.wikipedia.org/wiki/Logogram" target="_blank" rel="noopener">logographies</a>.</p> <h2>2. Tokenization</h2> <p><figure id="attachment_2570" aria-describedby="caption-attachment-2570" style="width: 1110px" class="wp-caption alignnone"><img decoding="async" class="size-full wp-image-2570" src="https://www.lexalytics.com/wp-content/uploads/2022/05/2.png" alt="" width="1110" height="821" srcset="https://www.lexalytics.com/wp-content/uploads/2022/05/2.png 1110w, https://www.lexalytics.com/wp-content/uploads/2022/05/2-300x222.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/05/2-1024x757.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/05/2-768x568.png 768w" sizes="(max-width: 1110px) 100vw, 1110px" /><figcaption id="caption-attachment-2570" class="wp-caption-text"><em>Fig. 2 – Most text analytics systems rely on rules-based algorithms to tokenize alphabetic languages, but logographic languages require the use of complex machine learning algorithms.</em></figcaption></figure></p> <p>Now that we know what language the text is in, we can break it up into pieces. <strong>Tokens</strong> are the individual units of meaning you’re operating on. This can be words, phonemes, or even full sentences. <strong>Tokenization</strong> is the process of breaking text documents apart into those pieces.</p> <p>In text analytics, tokens are most frequently just words. A sentence of 10 words, then, would contain 10 tokens. For deeper analytics, however, it’s often useful to expand your definition of a token. For Lexalytics, tokens can be:</p> <ul> <li>Words</li> <li>Punctuation (exclamation points <a href="https://www.lexalytics.com/technology/sentiment-analysis#negators-intensifiers">intensify sentiment</a>)</li> <li>Hyperlinks (https://…)</li> <li>Possessive markers (apostrophes)</li> </ul> <p>Tokenization is language-specific, and each language has its own tokenization requirements. English, for example, uses white space and punctuation to denote tokens, and is relatively simple to tokenize.</p> <p>In fact, most alphabetic languages follow relatively straightforward conventions to break up words, phrases and sentences. So, for most alphabetic languages, we can rely on rules-based tokenization.</p> <p>But not every language uses an alphabet.</p> <p>Many logographic (character-based) languages, such as Chinese, have no space breaks between words. Tokenizing these languages requires the use of <a href="https://www.lexalytics.com/resources/ml-nlp-whitepaper/" target="_blank" rel="noopener noreferrer">machine learning</a>, and is beyond the scope of this article.</p> <h2>3. Sentence Breaking</h2> <p><img decoding="async" class="alignnone size-full wp-image-2572" src="https://www.lexalytics.com/wp-content/uploads/2022/05/3.png" alt="" width="1112" height="832" srcset="https://www.lexalytics.com/wp-content/uploads/2022/05/3.png 1112w, https://www.lexalytics.com/wp-content/uploads/2022/05/3-300x224.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/05/3-1024x766.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/05/3-768x575.png 768w" sizes="(max-width: 1112px) 100vw, 1112px" /></p> <p>Once you’ve identified the tokens, you can tell where the sentences end. (See, look at that period right there, you knew exactly where the sentence ended, didn’t you Dr. Smart?)</p> <p>But look again at the second sentence above. Did it end with the period at the end of “Dr.?”</p> <p>Now check out the punctuation in that last sentence. There’s a period <em>and</em> a question mark right at the end of it!</p> <p>Point is, before you can run deeper text analytics functions (such as syntax parsing, #6 below), you must be able to tell where the boundaries are in a sentence. Sometimes it’s a simple process, and other times… not so much.</p> <p>Certain communication channels <cough> Twitter <cough> are particularly complicated to break down. We have ways of <strong>sentence breaking</strong> for social media, but we’ll leave that aside for now.</p> <h2>4. Part of Speech Tagging</h2> <p><img loading="lazy" decoding="async" class="alignnone size-full wp-image-2573" src="https://www.lexalytics.com/wp-content/uploads/2022/05/4.png" alt="" width="1110" height="737" srcset="https://www.lexalytics.com/wp-content/uploads/2022/05/4.png 1110w, https://www.lexalytics.com/wp-content/uploads/2022/05/4-300x199.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/05/4-1024x680.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/05/4-768x510.png 768w" sizes="(max-width: 1110px) 100vw, 1110px" /></p> <p>Once we’ve identifie<span style="font-style: normal !msorm;">d the language of a text document</span>, tokenized it, <span style="font-weight: normal !msorm;">and</span> <span style="font-weight: normal !msorm;">broken down the sentences, it’s time to tag it.</span></p> <p><strong>Part of Speech tagging</strong> (or PoS tagging) is the process of determining the part of speech of every token in a document, and then tagging it as such.</p> <p>For example, we use PoS tagging to figure out whether a given token represents a proper noun or a common noun, or if it’s a verb, an adjective, or something else entirely.</p> <p>Part of Speech tagging may sound simple, but much like an onion, you’d be surprised at the layers involved – and they just might make you cry. At Lexalytics, due to our breadth of language coverage, we’ve had to train our systems to understand 93 unique Part of Speech tags.</p> <h2>5. Chunking</h2> <p><img loading="lazy" decoding="async" class="alignnone size-full wp-image-2574" src="https://www.lexalytics.com/wp-content/uploads/2022/05/5.png" alt="" width="1112" height="832" srcset="https://www.lexalytics.com/wp-content/uploads/2022/05/5.png 1112w, https://www.lexalytics.com/wp-content/uploads/2022/05/5-300x224.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/05/5-1024x766.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/05/5-768x575.png 768w" sizes="(max-width: 1112px) 100vw, 1112px" /></p> <p>Let’s move on to the text analytics function known as <strong>Chunking</strong> (a few people call it <em>light parsing,</em> but we don’t). Chunking refers to a range of sentence-breaking systems that splinter a sentence into its component phrases (noun phrases, verb phrases, and so on).</p> <p>Before we move forward, I want to draw a quick distinction between Chunking and Part of Speech tagging in text analytics.</p> <ul> <li><strong>PoS tagging</strong> means assigning parts of speech to tokens</li> <li><strong>Chunking</strong> means assigning PoS-tagged tokens to phrases</li> </ul> <p>Here’s what it looks like in practice. Take the sentence:</p> <p><em>The tall man is going to quickly walk under the ladder.</em></p> <p>PoS tagging will identify <em>man </em>and <em>ladder</em> as nouns and <em>walk</em> as a verb.</p> <p>Chunking will return:  <em>[the tall man]_np [is going to quickly walk]_vp [under the ladder]_pp</em></p> <p>(<em>np</em> stands for “noun phrase,” <em>vp </em>stands for “verb phrase,” and <em>pp </em>stands for “prepositional phrase.”)</p> <p>Got that? Let’s move on.</p> <h2>6. Syntax Parsing</h2> <p><img loading="lazy" decoding="async" class="alignnone size-full wp-image-2575" src="https://www.lexalytics.com/wp-content/uploads/2022/05/6.png" alt="" width="1149" height="820" srcset="https://www.lexalytics.com/wp-content/uploads/2022/05/6.png 1149w, https://www.lexalytics.com/wp-content/uploads/2022/05/6-300x214.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/05/6-1024x731.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/05/6-768x548.png 768w" sizes="(max-width: 1149px) 100vw, 1149px" /></p> <p>The <strong>syntax parsing</strong> sub-function is a way to determine the structure of a sentence. In truth, <em>syntax parsing</em> is really just fancy talk for <em>sentence diagramming</em>. But it’s a critical preparatory step in <a href="https://www.lexalytics.com/technology/sentiment" target="_blank" rel="noopener noreferrer">sentiment analysis</a> and other natural language processing features.</p> <p>This becomes clear in the following example:</p> <ul> <li><strong>Apple </strong>was doing poorly until <em>Steve Jobs…</em></li> <li>Because <strong>Apple </strong>was doing poorly, <em>Steve Jobs…</em></li> <li><strong>Apple </strong>was doing poorly because <em>Steve Jobs…</em></li> </ul> <p>In the first sentence, <strong>Apple </strong>is negative, whereas <em>Steve Jobs </em>is positive.</p> <p>In the second, <strong>Apple </strong>is still negative, but <em>Steve Jobs </em>is now neutral.</p> <p>In the final example, both <strong>Apple </strong>and <em>Steve Jobs</em> are negative.</p> <p>Syntax parsing is one of the most computationally-intensive steps in text analytics. At Lexalytics, we use special unsupervised machine learning models, based on billions of input words and complex matrix factorization, to help us understand syntax just like a human would.</p> <h2>7. Sentence Chaining</h2> <p><img loading="lazy" decoding="async" class="alignnone size-full wp-image-2816" src="https://www.lexalytics.com/wp-content/uploads/2022/06/text-analytics-image0.png" alt="" width="1142" height="817" srcset="https://www.lexalytics.com/wp-content/uploads/2022/06/text-analytics-image0.png 1142w, https://www.lexalytics.com/wp-content/uploads/2022/06/text-analytics-image0-300x215.png 300w, https://www.lexalytics.com/wp-content/uploads/2022/06/text-analytics-image0-1024x733.png 1024w, https://www.lexalytics.com/wp-content/uploads/2022/06/text-analytics-image0-768x549.png 768w" sizes="(max-width: 1142px) 100vw, 1142px" /></p> <p>The final step in preparing unstructured text for deeper analysis is <strong>sentence chaining</strong>, sometimes known as <em>sentence relation</em>.</p> <p>Lexalytics utilizes a technique called “lexical chaining” to connect related sentences. Lexical chaining links individual sentences by each sentence’s strength of association to an overall <a href="https://www.lexalytics.com/technology/categorization">topic</a>.</p> <p>Even if sentences appear many paragraphs apart in a document, the lexical chain will flow through the document and help a machine detect overarching topics and quantify the overall “feel.”</p> <p>In fact, once you’ve drawn associations between sentences, you can run complex analyses, such as comparing and contrasting <a href="https://www.lexalytics.com/technology/sentiment" target="_blank" rel="noopener noreferrer">sentiment scores</a> and quickly generating accurate <a href="https://www.lexalytics.com/technology/summarization" target="_blank" rel="noopener noreferrer">summaries</a> of long documents.</p> <h2>Further reading and resources</h2> <p>Hungry for more information about text analytics?</p> <p>I’m a big fan of the <a href="https://en.wikipedia.org/wiki/Text_mining" target="_blank" rel="noopener noreferrer">Wikipedia article on this subject</a> (don’t tell my high school English teacher). Note that Wikipedia considers Text Analytics and Text Mining to be one and the same thing. I don’t necessarily agree with that position, but we’ll discuss that another time.</p> <p>You can also visit to our technology pages for more explanations of <a href="https://www.lexalytics.com/technology/sentiment-analysis/">sentiment analysis</a>, <a href="https://www.lexalytics.com/technology/entity-extraction">named entity recognition</a>, <a href="https://www.lexalytics.com/technology/summarization">summarization</a>, <a href="https://www.lexalytics.com/technology/intentions">intention extraction</a> and more.</p> <p>Lastly, if you’d like to see text analytics in action, feel free to play around with our <a href="https://www.lexalytics.com/nlp-demo" target="_blank" rel="noopener noreferrer">free online web demo</a>.</p> </div> </div> </article> <nav class="navigation post-navigation" aria-label="Posts"> <h2 class="screen-reader-text">Post navigation</h2> <div class="nav-links"><div class="nav-previous"><a href="https://www.lexalytics.com/blog/hospitality-voc-during-covid-19/" 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