Predictive analytics II CHAPTER Text. Web. and social Media analytics Learning Objectives for Chapter 5 Describe text analytics and understand the need for text mining Differentiate among text analytics, text mining, and data mining Understand the different application areas for text mining Know the process of carrying out a text mining project Appreciate the different methods to introduce structure to text-based data Describe sentiment analysi Develop familiarity with popular applications of sentiment analysis Learn the common methods for sentiment analysis Become familiar with speech analytics as it relates to sentiment analysis CHAPTER OVERVIE This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing the recent years, the unstructured data generated over the Internet of things(Web, sensor networks, RFID-enabled supply chain systems, surveillance networkS, etc. )is increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of heir business intelligence/analytics infrastructure Copyright C2018 Pearson Education, Inc
1 Copyright © 2018Pearson Education, Inc. Predictive Analytics II: Text, Web, and Social Media Analytics Learning Objectives for Chapter 5 ▪ Describe text analytics and understand the need for text mining ▪ Differentiate among text analytics, text mining, and data mining ▪ Understand the different application areas for text mining ▪ Know the process of carrying out a text mining project ▪ Appreciate the different methods to introduce structure to text-based data ▪ Describe sentiment analysis ▪ Develop familiarity with popular applications of sentiment analysis ▪ Learn the common methods for sentiment analysis ▪ Become familiar with speech analytics as it relates to sentiment analysis CHAPTER OVERVIEW This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing in the recent years, the unstructured data generated over the Internet of things (Web, sensor networks, RFID-enabled supply chain systems, surveillance networks, etc.) is increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure. CHAPTER 5
CHAPTER OUTLINE 5. 1 Opening Vignette: Machine versus Men on Jeopardy/ The Story of Watson 5.2 Text Analytics and Text Mining Overview 5.3 Natural Language Processing (NLP) 5.4 Text Mining Applications 5.5 Text Mining Process 5.6 Sentiment analysis 5.7 Web Mining Overview 5. 8 Search E 5.9 Web Usage Mining(Web Analytics) 5.10 Social Analytics ANSWERS TO END OF SECTION REVIEW QUESTIONS°··· Section 5. 1 Review Questions 1. What is Watson? What is special about it? Watson is a question answering(Qa)computer system developed by an IBM Research team and named after IBMs first president as part of a project called DeepQA. What makes it special is that it is able to compete at the human was able to defeat Ken Jennings, who held the record for the longest winn F champion level in real time on the tv quiz show, Jeopardy/; in fact, in 2011 streak in the game. Like Deep Blue has done with chess, Watson is showing that computer systems are getting quite good at demonstrating human-like intelligence What technologies were used in build ing Watson(both hardware and software)? Watson is built on the DeepQA framework. The hardware for this system involves a massively parallel processing architecture. In terms of software, Watson uses a variety of Al-related QA technologies, including text mining, natural language processing, question classification and decomposition, automatic source acquisition and evaluation, entity and relation detection, logical form generation, and knowled ge representation and reasoning Copyright C2018 Pearson Education, Inc
2 Copyright © 2018Pearson Education, Inc. CHAPTER OUTLINE 5.1 Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson 5.2 Text Analytics and Text Mining Overview 5.3 Natural Language Processing (NLP) 5.4 Text Mining Applications 5.5 Text Mining Process 5.6 Sentiment Analysis 5.7 Web Mining Overview 5.8 Search Engines 5.9 Web Usage Mining (Web Analytics) 5.10 Social Analytics ANSWERS TO END OF SECTION REVIEW QUESTIONS Section 5.1 Review Questions 1. What is Watson? What is special about it? Watson is a question answering (QA) computer system developed by an IBM Research team and named after IBM’s first president as part of a project called DeepQA. What makes it special is that it is able to compete at the human champion level in real time on the TV quiz show, Jeopardy!; in fact, in 2011, it was able to defeat Ken Jennings, who held the record for the longest winning streak in the game. Like Deep Blue has done with chess, Watson is showing that computer systems are getting quite good at demonstrating human-like intelligence. 2. What technologies were used in building Watson (both hardware and software)? Watson is built on the DeepQA framework. The hardware for this system involves a massively parallel processing architecture. In terms of software, Watson uses a variety of AI-related QA technologies, including text mining, natural language processing, question classification and decomposition, automatic source acquisition and evaluation, entity and relation detection, logical form generation, and knowledge representation and reasoning
3. What are the innovative characteristics of Deep a architecture that made watson superior The DeepQA architecture involves massive parallelism, many experts, pervasive confidence estimation, and integration of the-latest-and-greatest in-text analytics, involving both shallow and deep semantic knowledge. As implemented in Watson, DeepQa brings more than 100 different techniques for analyzing natural language identifying sources, finding anking ypotheses. More important than any nd generating hypotheses, find ing and scoring particular technique is the combination of overlapping approaches that can bring their strengths to bear and contribute to improvements in accuracy, confidence, and Why did I BM spend all that time and money to build Watson? Where is the rol? IBMs goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society. The techniques IBM developed with DeepQA and Watson are relevant in a wide variety of domains central to IBMs mission. For example, IBM is currently working on a version of Watson to take on surmountable problems in healthcare and medicine. If successful, this could give IBM a distinct competitive advantage in this important technological application ar Section 5.2 Review Questions What is text analytics? How does it differ from text mining? Text analytics is a concept that includes information retrieval (e. g, searching and identifying relevant documents for a given set of key terms)as well as information extraction, data mining, and Web mining. By contrast, text mining is primarily focused on discovering new and useful knowledge from textual data sources. The overarching goal for both text analytics and text mining is to turn unstructured textual data into actionable information through the application of natural language processing(NLP)and analytics. However, text analytics is a broader term because of its inclusion of information retrieval. you can think of text analytics as a combination of information retrieval plus text mining 2. What is text mining? How does it differ from data mining? Text mining is the application of data mining to unstructured, or less structured text files. As the names indicate, text mining analyzes words, and data mini alyzes numeric data Copyright C2018 Pearson Education, Inc
3 Copyright © 2018Pearson Education, Inc. 3. What are the innovative characteristics of DeepQA architecture that made Watson superior? The DeepQA architecture involves massive parallelism, many experts, pervasive confidence estimation, and integration of the-latest-and-greatest in-text analytics, involving both shallow and deep semantic knowledge. As implemented in Watson, DeepQA brings more than 100 different techniques for analyzing natural language, identifying sources, finding and generating hypotheses, finding and scoring evidence, and merging and ranking hypotheses. More important than any particular technique is the combination of overlapping approaches that can bring their strengths to bear and contribute to improvements in accuracy, confidence, and speed. 4. Why did IBM spend all that time and money to build Watson? Where is the ROI? IBM’s goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society. The techniques IBM developed with DeepQA and Watson are relevant in a wide variety of domains central to IBM’s mission. For example, IBM is currently working on a version of Watson to take on surmountable problems in healthcare and medicine. If successful, this could give IBM a distinct competitive advantage in this important technological application area. Section 5.2 Review Questions 1. What is text analytics? How does it differ from text mining? Text analytics is a concept that includes information retrieval (e.g., searching and identifying relevant documents for a given set of key terms) as well as information extraction, data mining, and Web mining. By contrast, text mining is primarily focused on discovering new and useful knowledge from textual data sources. The overarching goal for both text analytics and text mining is to turn unstructured textual data into actionable information through the application of natural language processing (NLP) and analytics. However, text analytics is a broader term because of its inclusion of information retrieval. You can think of text analytics as a combination of information retrieval plus text mining. 2. What is text mining? How does it differ from data mining? Text mining is the application of data mining to unstructured, or less structured, text files. As the names indicate, text mining analyzes words; and data mining analyzes numeric data
Why is the popularity of text mining as a bI tool increasing? Text mining as a bi tool is increasing because of the rapid growth in text data and availability of sophisticated BI tools. The benefits of text mining are obvious in the areas where very large amounts of textual data are being generated, such as law(court orders), academic research(research articles), finance( quarterly technology(patent files), and marketing(customer comments)actions) reports), medicine(discharge summaries), biology(molecular interactions) What are some popular application areas of text mining? within text by looking for predefined sequences in text via pattern matching Topic tracking. Based on a user profile and documents that a use text mining can predict other documents of interest to the user er views, Summarization. Summarizing a document to save time on the part of the Categorization. Identifying the main themes of a document and then placing the document into a predefined set of categories based on those themes Clustering Grouping similar documents without having a predefined set of categories Concept linking. Connects related documents by identifying their shared concepts and, by doing So, helps users find information that they perhaps would not have found using trad itional search methods Question answering. Finding the best answer to a given question through knowledge-driven pattern matchin Section 5.3 Review Questions What is NLP? Natural language processing(NLP)is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of"understand ing" the natural human language, with the view of converting depictions of human language(such as textual documents)into more formal representations(in the form of numeric and symbolic data)that are easier for computer programs to manipulate Copyright C2018 Pearson Education, Inc
4 Copyright © 2018Pearson Education, Inc. 3. Why is the popularity of text mining as a BI tool increasing? Text mining as a BI tool is increasing because of the rapid growth in text data and availability of sophisticated BI tools. The benefits of text mining are obvious in the areas where very large amounts of textual data are being generated, such as law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), and marketing (customer comments). 4. What are some popular application areas of text mining? • Information extraction. Identification of key phrases and relationships within text by looking for predefined sequences in text via pattern matching. • Topic tracking. Based on a user profile and documents that a user views, text mining can predict other documents of interest to the user. • Summarization. Summarizing a document to save time on the part of the reader. • Categorization. Identifying the main themes of a document and then placing the document into a predefined set of categories based on those themes. • Clustering. Grouping similar documents without having a predefined set of categories. • Concept linking. Connects related documents by identifying their shared concepts and, by doing so, helps users find information that they perhaps would not have found using traditional search methods. • Question answering. Finding the best answer to a given question through knowledge-driven pattern matching. Section 5.3 Review Questions 1. What is NLP? Natural language processing (NLP) is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of “understanding” the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate
How does nlP relate to text mining? Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering association, and sequence discovery to extract knowledge from it 3. What are some of the benefits and challenges of NLP? NLP moves beyond syntax-driven text manipulation(which is often called word counting)to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. The challenges include Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech because the part of speech depends not only on the definition of the term but also on the context within which it is used Text segmentation. Some written languages, such as Chinese, Japanese and Thai, do not have single-word boundaries Word sense disambiguation. Many words have more than one meaning Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered Choosing the most appropriate structure usually requires a fusion of semantic and contextual information Imperfect or irregular input. Foreign or regional accents and vocal imped iments in speech and typographical or gramma ical errors in texts make the processing of the language an even more difficult task Speech acts. A sentence can often be considered an action by the speake The sentence structure alone may not contain enough information to define this action 4. What are the most common tasks addressed by Following are among the most popular tasks ering Automatic summarization Natural language generation Copyright o201& Pearson Education, Inc
5 Copyright © 2018Pearson Education, Inc. 2. How does NLP relate to text mining? Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering, association, and sequence discovery to extract knowledge from it. 3. What are some of the benefits and challenges of NLP? NLP moves beyond syntax-driven text manipulation (which is often called “word counting”) to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. The challenges include: • Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech because the part of speech depends not only on the definition of the term but also on the context within which it is used. • Text segmentation. Some written languages, such as Chinese, Japanese, and Thai, do not have single-word boundaries. • Word sense disambiguation. Many words have more than one meaning. Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used. • Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered. Choosing the most appropriate structure usually requires a fusion of semantic and contextual information. • Imperfect or irregular input. Foreign or regional accents and vocal impediments in speech and typographical or grammatical errors in texts make the processing of the language an even more difficult task. • Speech acts. A sentence can often be considered an action by the speaker. The sentence structure alone may not contain enough information to define this action. 4. What are the most common tasks addressed by NLP? Following are among the most popular tasks: • Question answering • Automatic summarization • Natural language generation