MS (CS) - Text Analytics, Text mining

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Lexalytics Joins InMoment | Accelerating Global Delivery of Structured and Unstructured Text LEARN MORE → (https://inmoment.com/inmoment-lexalytics/) Languages and Features (/technology/features) Industry Packs (/technology/industry-packs) Text Analytics (/technology/text-analytics) Machine Learning (/technology/machine-learning) Sentiment Analysis (/technology/sentiment) Named Entity Extraction (/technology/entity-extraction) Categorization (/technology/categorization) Intention Extraction (/technology/intentions) Summarization (/technology/summarization) Tuning Guides → (/support/tuning/) Text Analytics What is Text Analytics? Text analytics is the process of transforming unstructured text documents into usable, structured data. Text analysis works by breaking apart sentences and phrases into their components, and then evaluating each part’s role and meaning using complex software rules and machine learning algorithms. Text analytics forms the foundation of numerous natural language processing (NLP) features, including named entity recognition, categorization, and sentiment analysis. In broad terms, these  More about our technology Text Analytics | Lexalytics https://www.lexalytics.com/technology/text-analytics 1 of 15 15-Apr-22, 12:15 PM NLP features aim to answer four questions: • Who is talking? • What are they talking about? • What are they saying about those subjects? • How do they feel? Data analysts and other professionals use text mining tools to derive useful information and context-rich insights from large volumes of raw text, such as social media comments, online reviews, and news articles. In this way, text analytics software forms the backbone of business intelligence programs, including voice of customer/customer experience management, social listening and media monitoring, and voice of employee/workforce analytics. This article will cover the basics of text analytics, starting with the difference between text analytics, text mining, and natural language processing. Then we’ll explain the seven functions of text analytics and explore some basic applications of text mining. Finally, we’ll tell you where you can try text analytics for free and share some resources for further reading. Table of contents: • Defining terms ◦ Text analytics ◦ Text mining ◦ Natural language processing • How text analytics works ◦ Language identification ◦ Tokenization ◦ Sentence breaking ◦ Part of speech tagging ◦ Chunking ◦ Syntax parsing ◦ Sentence chaining • Basic applications ◦ Voice of customer ◦ Social media monitoring ◦ Voice of employee • Further reading ◦ Try text analytics and text mining for free ◦ Learn more about text analytics  More about our technology Text Analytics | Lexalytics https://www.lexalytics.com/technology/text-analytics 2 of 15 15-Apr-22, 12:15 PM What is the difference between text mining, text analytics and natural language processing? Text mining describes the general act of gathering useful information from text documents. Text analytics refers to the actual computational processes of breaking down unstructured text documents, such as tweets, articles, reviews and comments, so they can be analyzed further. Natural language processing (NLP) is how a computer understands the underlying meaning of those text documents: who’s talking, what they’re talking about, and how they feel about those subjects. As a term, text mining is often used interchangeably with text analytics. In most cases, that’s fine. But there is a difference. If text mining refers to collecting useful information from text documents, text analytics is how a computer actually transforms those raw words into information. Meanwhile, the low-level computational functions of text analytics form the foundation of natural language processing features, such as sentiment analysis, named entity recognition, categorization, and theme analysis. How does text analytics work? Text analytics starts by breaking down each sentence and phrase into its basic parts. Each of these components, including parts of speech, tokens, and chunks, serve a vital role in accomplishing deeper natural language processing and contextual analysis. There are seven computational steps involved in preparing an unstructured text document for deeper analysis: 1. Language Identification 2. Tokenization 3. Sentence breaking 4. Part of Speech tagging 5. Chunking 6. Syntax parsing 7. Sentence chaining Some text analytics functions are accomplished exclusively through rules-based software systems. Other functions require machine learning models (including deep learning algorithms) to achieve.  More about our technology Text Analytics | Lexalytics https://www.lexalytics.com/technology/text-analytics 3 of 15 15-Apr-22, 12:15 PM