Relationships between web analytics text mining and sentiment analysis

16 Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

Chapter 7:

Text Analytics, Text Mining, and Sentiment Analysis

Learning Objectives for Chapter 7

1. Describe text mining and understand the need for text mining

2. Differentiate among text analytics, text mining, and data mining

3. Understand the different application areas for text mining

4. Know the process of carrying out a text mining project

5. Appreciate the different methods to introduce structure to text-based data

6. Describe sentiment analysis

7. Develop familiarity with popular applications of sentiment analysis

8. Learn the common methods for sentiment analysis

9. Become familiar with speech analytics as it relates to sentiment analysis

10. Learn three facets of Web analytics—content, structure, and usage mining

11. Know social analytics including social media and social network analyses

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 recent years, the unstructured data generated over the Internet of Things (IoT) (Web, sensor networks, radio-frequency identification [RFID]–enabled supply chain systems, surveillance networks, etc.) are 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 OUTLINE

7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into

Near-Real-Time Sales

7.2 Text Analytics and Text Mining Overview

7.3 Natural Language Processing (NLP)

7.4 Text Mining Applications

7.5 Text Mining Process

7.6 Sentiment Analysis

7.7 Web Mining Overview

7.8 Search Engines

7.9 Web Usage Mining

7.10 Social Analytics

ANSWERS TO END OF SECTION REVIEW QUESTIONS( ( ( ( ( (

Section 7.1 Review Questions

1. According to the vignette and based on your opinion, what are the challenges that the food industry is facing today?

Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.

2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?

Analytics can serve dual purposes by both tracking customer interest in the brand as well as providing valuable feedback on customer preferences. An analytics system can be used to evaluate the traffic to various brand marketing campaigns (website or social) that play a pivotal role in ensuring that products are being shown to new potential buyers and reminding existing customers of their value. An analytics system can also be used to help gather customer feedback and perception information on a brand in general or products in particular. This valuable information can be used as a part of both marketing and product design.

3. What were and still are the main objectives for Amadori to embark into analytics? What were the results?

The company’s main objectives were to market more effectively to potential customers and create direct communications through social media and other channels with current customers to start a dialogue. The case illustrates how an analytics system integrated with thoughtful website design can help a company meet these goals.

4. Can you think of other businesses in the food industry that utilize analytics to become more competitive and customer focused? If not, an Internet search could help find relevant information to answer this question.

Student opinions and Web searches will vary, but will show similar strategies for packaged foods as well as fast foods in the US.

Section 7.2 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.

3. Why is the popularity of text mining as an analytics tool increasing?

Text mining as a BI 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 7.3 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.

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.

• Natural language understanding.

• Machine translation.

• Foreign language reading.

• Foreign language writing.

• Speech recognition.

• Text-to-speech.

• Text proofing.

• Optical character recognition.

Section 7.4 Review Questions

5. List and briefly discuss some of the text mining applications in marketing.

Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

Text mining has become invaluable for customer relationship management. Companies can use text mining to analyze rich sets of unstructured text data, combined with the relevant structured data extracted from organizational databases, to predict customer perceptions and subsequent purchasing behavior.

6. How can text mining be used in security and counterterrorism?

Students may use the introductory case in this answer.

In 2007, EUROPOL developed an integrated system capable of accessing, storing, and analyzing vast amounts of structured and unstructured data sources in order to track transnational organized crime.

Another security-related application of text mining is in the area of deception detection.

7. What are some promising text mining applications in biomedicine?

As in any other experimental approach, it is necessary to analyze this vast amount of data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.

Section 7.5 Review Questions

8. What are the main steps in the text mining process?

See Figure 7.6 (p. 309). Text mining entails three tasks:

· Establish the Corpus: Collect and organize the domain-specific unstructured data

· Create the Term–Document Matrix: Introduce structure to the corpus

· Extract Knowledge: Discover novel patterns from the T-D matrix

9. What is the reason for normalizing word frequencies? What are the common methods for normalizing word frequencies?

The raw indices need to be normalized in order to have a more consistent TDM for further analysis. Common methods are log frequencies, binary frequencies, and inverse document frequencies.

10. What is SVD? How is it used in text mining?

Singular value decomposition (SVD), which is closely related to principal components analysis, reduces the overall dimensionality of the input matrix (number of input documents by number of extracted terms) to a lower dimensional space, where each consecutive dimension represents the largest degree of variability (between words and documents) possible

11. What are the main knowledge extraction methods from corpus?

The main categories of knowledge extraction methods are classification, clustering, association, and trend analysis.

Section 7.6 Review Questions

12. What is sentiment analysis? How does it relate to text mining?

Sentiment analysis tries to answer the question, “What do people feel about a certain topic?” by digging into opinions of many using a variety of automated tools. It is also known as opinion mining, subjectivity analysis, and appraisal extraction

Sentiment analysis shares many characteristics and techniques with text mining. However, unlike text mining, which categorizes text by conceptual taxonomies of topics, sentiment classification generally deals with two classes (positive versus negative), a range of polarity (e.g., star ratings for movies), or a range in strength of opinion.

13. What are the most popular application areas for sentiment analysis? Why?

Customer relationship management (CRM) and customer experience management are popular “voice of the customer (VOC)” applications. Other application areas include “voice of the market (VOM)” and “voice of the employee (VOE).”

14. What would be the expected benefits and beneficiaries of sentiment analysis in politics?

Opinions matter a great deal in politics. Because political discussions are dominated by quotes, sarcasm, and complex references to persons, organizations, and ideas, politics is one of the most difficult, and potentially fruitful, areas for sentiment analysis. By analyzing the sentiment on election forums, one may predict who is more likely to win or lose. Sentiment analysis can help understand what voters are thinking and can clarify a candidate’s position on issues. Sentiment analysis can help political organizations, campaigns, and news analysts to better understand which issues and positions matter the most to voters. The technology was successfully applied by both parties to the 2008 and 2012 American presidential election campaigns.

15. What are the main steps in carrying out sentiment analysis projects?

The first step when performing sentiment analysis of a text document is called sentiment detection, during which text data is differentiated between fact and opinion (objective vs. subjective). This is followed by negative-positive (N-P) polarity classification, where a subjective text item is classified on a bipolar range. Following this comes target identification (identifying the person, product, event, etc. that the sentiment is about). Finally come collection and aggregation, in which the overall sentiment for the document is calculated based on the calculations of sentiments of individual phrases and words from the first three steps.

16. What are the two common methods for polarity identification? What is the main difference between the two?

Polarity identification can be done via a lexicon (as a reference library) or by using a collection of training documents and inductive machine learning algorithms. The lexicon approach uses a catalog of words, their synonyms, and their meanings, combined with numerical ratings indicating the position on the N-P polarity associated with these words. In this way, affective, emotional, and attitudinal phrases can be classified according to their degree of positivity or negativity. By contrast, the training-document approach uses statistical analysis and machine learning algorithms, such as neural networks, clustering approaches, and decision trees to ascertain the sentiment for a new text document based on patterns from previous “training” documents with assigned sentiment scores.

Section 7.7 Review Questions

17. What are some of the main challenges the Web poses for knowledge discovery?

• The Web is too big for effective data mining.

• The Web is too complex.

• The Web is too dynamic.

• The Web is not specific to a domain.

• The Web has everything.

18. What is Web mining? How does it differ from regular data mining or text mining?

Web mining is the discovery and analysis of interesting and useful information from the Web and about the Web, usually through Web-based tools. Text mining is less structured because it’s based on words instead of numeric data.

19. What are the three main areas of Web mining?

The three main areas of Web mining are Web content mining, Web structure mining, and Web usage (or activity) mining.

20. Identify three application areas for Web mining (at the bottom of Figure 8.1). Based on your own experiences, comment on their use cases in business settings.

(Since there are several application areas, this answer will vary for different students. Following is one possible answer.)

Three possible application areas for Web mining include sentiment analysis, clickstream analysis, and customer analytics. Clickstream analysis helps to better understand user behavior on a website. Sentiment analysis helps us understand the opinions and affective state of users on a system. Customer analytics helps to provide solutions for sales, service, marketing, and product teams, and optimize the customer life cycles. The use cases for these applications center on user experience, and primarily affect customer service and customer relationship management functions of an organization.

21. What is Web content mining? How can it be used for competitive advantage?

Web content mining refers to the extraction of useful information from Web pages. The documents may be extracted in some machine-readable format so that automated techniques can generate some information about the Web pages. Collecting and mining Web content can be used for competitive intelligence (collecting intelligence about competitors’ products, services, and customers), which can give your organization a competitive advantage.

22. What is Web structure mining? How does it differ from Web content mining?

Web structure mining is the process of extracting useful information from the links embedded in Web documents. By contrast, Web content mining involves analysis of the specific textual content of web pages. So, Web structure mining is more related to navigation through a website, whereas Web content mining is more related to text mining and the document hierarchy of a particular web page.

Section 7.8 Review Questions

1. What is a search engine? Why are search engines critically important for today’s businesses?

A search engine is a software program that searches for documents (Internet sites or files) based on the keywords (individual words, multi-word terms, or a complete sentence) that users have provided that have to do with the subject of their inquiry. This is the most prominent type of information retrieval system for finding relevant content on the Web. Search engines have become the centerpiece of most Internet-based transactions and other activities. Because people use them extensively to learn about products and services, it is very important for companies to have prominent visibility on the Web; hence the major effort of companies to enhance their search engine optimization (SEO).

2. What is a Web crawler? What is it used for? How does it work?

A Web crawler (also called a spider or a Web spider) is a piece of software that systematically browses (crawls through) the World Wide Web for the purpose of finding and fetching Web pages. It starts with a list of “seed” URLs, goes to the pages of those URLs, and then follows each page’s hyperlinks, adding them to the search engine’s database. Thus, the Web crawler navigates through the Web in order to construct the database of websites.

3. What is “search engine optimization”? Who benefits from it?

Search engine optimization (SEO) is the intentional activity of affecting the visibility of an e-commerce site or a website in a search engine’s natural (unpaid or organic) search results. It involves editing a page’s content, HTML, metadata, and associated coding to both increase its relevance to specific keywords and to remove barriers to the indexing activities of search engines. In addition, SEO efforts include promoting a site to increase its number of inbound links. SEO primarily benefits companies with e-commerce sites by making their pages appear toward the top of search engine lists when users query.

4. What things can help Web pages rank higher in search engine results?

Cross-linking between pages of the same website to provide more links to the most important pages may improve its visibility. Writing content that includes frequently searched keyword phrases, so as to be relevant to a wide variety of search queries, will tend to increase traffic. Updating content so as to keep search engines crawling back frequently can give additional weight to a site. Adding relevant keywords to a Web page’s metadata, including the title tag and metadescription, will tend to improve the relevancy of a site’s search listings, thus increasing traffic. URL normalization of Web pages so that they are accessible via multiple URLs. Using canonical link elements and redirects can help make sure links to different versions of the URL all count toward the page’s link popularity score.

Section 7.9 Review Questions

1. What are the three types of data generated through Web page visits?

· Automatically generated data stored in server access logs, referrer logs, agent logs, and client-side cookies

· User profiles

· Metadata, such as page attributes, content attributes, and usage data.

2. What is clickstream analysis? What is it used for?

Analysis of the information collected by Web servers can help us better understand user behavior. Analysis of this data is often called clickstream analysis. By using the data and text mining techniques, a company might be able to discern interesting patterns from the clickstreams.

3. What are the main applications of Web mining?

· Determine the lifetime value of clients.

· Design cross-marketing strategies across products.

· Evaluate promotional campaigns.

· Target electronic ads and coupons at user groups based on user access patterns.

· Predict user behavior based on previously learned rules and users’ profiles.

· Present dynamic information to users based on their interests and profiles.

4. What are commonly used Web analytics metrics? What is the importance of metrics?

There are four main categories of Web analytic metrics:

· Website usability: How were they using my website? These involve page views, time on site, downloads, click map, and click paths.

· Traffic sources: Where did they come from? These include referral websites, search engines, direct, offline campaigns, and online campaigns.

· Visitor profiles: What do my visitors look like? These include keywords, content groupings, geography, time of day, and landing page profiles.

· Conversion statistics: What does all this mean for the business? Metrics include new visitors, returning visitors, leads, sales/conversions, and abandonments.

These metrics are important because they provide access to a lot of valuable marketing data, which can be leveraged for better insights to grow your business and better document your ROI. The insight and intelligence gained from Web analytics can be used to effectively manage the marketing efforts of an organization and its various products or services.

Section 7.10 Review Questions

1. What is meant by social analytics? Why is it an important business topic?

From a philosophical perspective, social analytics focuses on a theoretical object called a “socius,” a kind of “commonness” that is neither a universal account nor a communality shared by every member of a body. Thus, social analytics in this sense attempts to articulate the differences between philosophy and sociology. From a BI perspective, social analytics involves “monitoring, analyzing, measuring and interpreting digital interactions and relationships of people, topics, ideas and content.” In this perspective, social analytics involves mining the textual content created in social media (e.g., sentiment analysis, natural language processing) and analyzing socially established networks (e.g., influencer identification, profiling, prediction). This is an important business topic because it helps companies gain insight about existing and potential customers’ current and future behaviors, and about the likes and dislikes toward a firm’s products and services.

2. What is a social network? What is the need for SNA?

A social network is a social structure composed of individuals/people (or groups of individuals or organizations) linked to one another with some type of connections/relationships. Social network analysis (SNA) is the systematic examination of social networks. Dating back to the 1950s, social network analysis is an interdisciplinary field that emerged from social psychology, sociology, statistics, and graph (network) theory.

3. What is social media? How does it relate to Web 2.0?

Social media refers to the enabling technologies of social interactions among people in which they create, share, and exchange information, ideas, and opinions in virtual communities and networks. It is a group of Internet-based software applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content.

4. What is social media analytics? What are the reasons behind its increasing popularity?

Social media analytics refers to the systematic and scientific ways to consume the vast amount of content created by Web-based social media outlets, tools, and techniques for the betterment of an organization’s competitiveness. Data includes anything posted in a social media site.

5. How can you measure the impact of social media analytics?

First, determine what your social media goals are. From here, you can use analysis tools such as descriptive analytics, social network analysis, and advanced (predictive, text examining content in online conversations), and ultimately prescriptive analytics tools.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION( (

Application Case 7.1: Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analytics to Drive Content and Consumer Insight

1. What does Netflix do? How did they evolve into this current business model?

Netflix is a provider and creator of streaming digital content for end-users. The company began as a subscription-based DVD rental company and expanded into digital streaming. Changes in the digital streaming market made it more economically beneficial for the company to also expand into content creation.

2. In the case of Netflix, what was it meant to be data-driven and customer-focused?

Four Netflix, being data-driven means that decisions on new content being created or licensed (as well as maintaining existing licenses) should be based on data directly related to customer preferences (based on actual use as well as preferred genres). The decision on how to spend these limited licensing dollars must be made to create the greatest benefit for the greatest number of users in order to maintain current subscriptions as well as drive new sign-ups. This focus on customer demands also meets the aspect of customer focus.

3. How did Netflix use Teradata technologies in its analytics endeavors?

The Teradata solution provides two distinct advantages for Netflix. The first is the ability to use an existing, robust cloud-based system to perform analytics functions. This provides security both in terms of redundancy as well as confidence in the technology itself. The second is the ability to use Teradata’s robust discovery engine to perform analytics functions to create a better understanding of customer types and their preferences.

Application Case 7.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World

1. What are the common challenges that broadcasting companies are facing today? How can analytics help to alleviate these challenges?

Broadcasters are faced with the need to maintain the attention of viewers by providing quality program that meets their current desires, desires that are always in flux. An analytics system can help to alleviate these challenges by evaluating viewers current tastes and desires for future programming.

2. How did AMC leverage analytics to enhance its business performance?

AMC has been able to successfully leverage analytics to better understand their customers and use this understanding to market effectively to them as well as ensure that they provide content that meets viewers desires. These analytics systems allow the broadcaster to better understand what different customers want in specific groupings, but also how to market their services effectively to individuals within these groupings.

3. What were the types of text analytics and text minisolutions developed by AMC networks? Can you think of other potential uses of text mining applications in the broadcasting industry?

These details are not discussed in the case, but students may be able to identify some of this information through independent research. In general, the analytics being used incorporate data from internal and external sources to determine viewership of individual programs as well as purchases of digital programs.

Application Case 7.3: Mining for Lies

1. Why is it difficult to detect deception?

Humans tend to perform poorly at deception-detection tasks. This phenomenon is exacerbated in text-based communications. Although some people believe that they can readily identify those who are not being truthful, a summary of deception research showed that, on average, people are only 54 percent accurate in making veracity determinations.

2. How can text/data mining be used to detect deception in text?

Through a process known as message feature mining, statements are transcribed for processing, then cues are extracted and selected. Text processing software identifies cues in statements and generates quantified cues. Classification models are trained and tested on quantified cues, and based on this, statements are labeled as truthful or deceptive (e.g., by law enforcement personnel). The feature-selection methods along with 10-fold cross-validation allow researchers to compare the prediction accuracy of different data mining methods (for example, neural networks).

3. What do you think are the main challenges for such an automated system?

One challenge is that the training the system depends on humans to ascertain the truthfulness of statements in the training data itself. You can’t know for sure whether these statements are true or false, so you may be using incorrect training samples when “teaching” the machine learning system to predict lies in new text data. (This answer will vary by student.)

Application Case 7.4: The Magic Behind the Magic: Instant Access to Information Helps the Orlando Magic Up their Game and the Fan’s Experience

1. According to the application case, what were the main challenges the Orlando Magic was facing?

The primary challenges are both financial, with a need to maximize both ticket sales and concessions/merchandising. These challenges can be addressed by tailoring services to meet the needs of customers.

2. How did analytics help the Orlando Magic to overcome some of its most significant challenges on and off the court?

In order to meet the challenge of ensuring that season ticket purchases continue, the team used a predictive analysis decision tree model to identify customers that may not renew, and those individuals could receive additional sales and marketing attention. Additionally, coaching staff use analytic tools to better understand all aspects of the game itself including player strengths and weaknesses and opponent strategies.

3. Can you think of other uses of analytics in sports and especially in the case of the Orlando Magic? You can search the Web to find some answers to this question.

Student research and responses will vary but may include the idea to use analytics when selecting new players or determining if players ready for contract renewal should be retained.

Application Case 7.5: Research Literature Survey with Text Mining

1. How can text mining be used to ease the task of literature review?

Text mining enables a semiautomated analysis of large volumes of published literature. Clustering was used in this study to identify the natural groupings of the articles and list the most descriptive terms that characterized those clusters. This led to discovery and exploration of interesting patterns using tabular and graphical representation of their findings. Use of text and data mining can thus speed up and simplify the literature review process for academic researchers.

2. What are the common outcomes of a text mining project on a specific collection of journal articles? Can you think of other potential outcomes not mentioned in this case?

Common outcomes include identifying natural clusters of similar articles, helping to identify the optimal number of cluster classifications. Using text mining, you can answer questions such as “Are there clusters that represent different research themes specific to a single journal?” and “Is there a time-varying characterization of those clusters?” Text mining also has other possible applications in literature reviews. For example, sentiment analysis can help to identify positive and negative judgments. Text mining can be used to build taxonomies of concepts and terms within and between research articles. You can find common themes by author as well as by journal.

Application Case 7.6: Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon

1. How did Wimbledon use analytics capabilities to enhance viewers’ experience?

Wimbledon undertook a number of analytics projects in order to enhance the viewer experience for the tournament. One project was a redesign of the website for the tournament. Based on their understanding of current users they were able to determine that the majority of users were still accessing the site using desktop browsers, and so were able to work on optimizing the experience for that platform (as well as accommodating the growth in mobile users). Next they were able to tap in to a huge amount of data that was being collected or had been collected over time. Data and analysis from the match came in in real time and was analyzed and displayed as information to viewers. Additionally, NLP systems allowed the tournament to search its vast archives of facts and details that could be retrieved and presented in real time. Finally, Wimbledon used their understanding of the potential demand for this service to make appropriate decisions about housing both the broadcasting and analytics portions of the service in the cloud to provide the necessary bandwidth and security.

2. What were the challenges, proposed solution, and obtained results?

Many of the challenges were technical, but those were overcome through the use of existing, trusted IT partners to provide services and create a unique digital presence. The results were very positive, with a significant number of online visitors and a 98% growth in total visits over the previous year.

Application Case 7.7: Delivering Individualized Content and Driving Digital Engagement: How Barbour Collected More Than 49,000 New Leads in One Month with Teradata Interactive

1. What does Barbour do? What was the challenge Barbour was facing?

Barbour is an English heritage and lifestyle brand renowned for waterproof outerwear that has recently expanded into other luxury fashion goods. The company’s challenge was establishing a direct relationship with its customers because in the past all sales had been made through distributors. While in e-commerce site was launched in 2013, connecting with customers in a crowded digital market space was difficult.

2. What was the proposed analytics solution?

The company worked with partner Teradata to create a lead nurturing program. The goal was both to drive immediate sales as well as to gather information that could create more meaningful long-term relationships with customers. The company had access to historical customer information that was generally seen as incomplete. Teradata was able to begin with this information and create marketing campaigns that also collected additional data that allowed for future, more personalized content to be driven to customers through a customer lifecycle program. These activities were integrated with existing marketing and social media campaigns.

3. What were the results?

The results were very positive and over a one-month period the company was able to collect more than 49,700 leads within their primary European regions. In addition to very positive responses (60% click through rates) the company was also able to collect and begin analysis on additional customer data that can be used in the lifecycle program.

Application Case 7.8: Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy

1. How can social media analytics be used in the consumer products industry?

Social media analytics can be incredibly useful for consumer products because it allows the producers/retailers to better understand their customers and connect with them individually based on their preferences.

2. What do you think are the key challenges, potential solutions, and probable results in applying social media analytics in consumer products and services firms?

It’s

Student opinions will vary, but responses will focus on:

· Challenges - crowded market spaces with data in multiple formats from multiple sources

· potential solutions - focus on analytics and the ability to better understand customer needs at both the aggregate and individual level

· probable results - can be very positive if analytic efforts are successful in identifying customer preferences and targeting individual customers or unique groups with marketing or social media content that drives interest in the brand

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION( ( (

1. Explain the relationship among data mining, text mining, and sentiment analysis.

Technically speaking, data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract and identify useful information and subsequent knowledge (or patterns) from large sets of data. Data mining is the general concept. Text mining is a specific application of data mining: applying it to unstructured text files. Sentiment analysis is a specialized form of text and data mining that identifies and classifies terms in text sources according to sentiment (e.g. judgment, opinion, and emotional content).

2. In your own words, define text mining, and discuss its most popular applications.

Students’ answers will vary.

3. What does it mean to induce structure into text-based data? Discuss the alternative ways of inducing structure into them.

Text mining, like other data mining approaches, are inductive approaches for finding patterns and trends in data. One difference between text mining and other data mining approaches is the use of natural language processing.

Text-based data is inherently unstructured and must be converted to a structured format for predictive modeling or other type of analysis. The third step of the 3-step text mining process utilizes inductive algorithms to classify and structure the corpus of text sources so that knowledge can be extracted.

Four possible approaches for inducing structure in text in order to extract knowledge are (a) classification (grouping terms into predefined categories), (b) clustering (coming up with “natural” groupings), (c) association rule learning (finding frequent combinations of terms), and (d) trend analysis (recognizing concept distributions based on specific collections of documents).

4. What is the role of NLP in text mining? Discuss the capabilities and limitations of NLP in the context of text mining.

NLP is an important component of text mining. 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. The goal of NLP is to move 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.

5. List and discuss three prominent application areas for text mining. What is the common theme among the three application areas you chose?

National security and counterterrorism: The FBI’s system is expected to create a gigantic data warehouse along with a variety of data and text mining modules to meet the knowledge discovery needs of federal, state, and local law enforcement agencies.

Biomedical: A system extracts disease–gene relationships from literature accessed via MEDLINE.

Marketing: Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

The value of all of these applications is significantly increased by extracting knowledge from huge volumes of text-data and documents through the use of text mining tools.

6. What is sentiment analysis? How does it relate to text mining?

Sentiment analysis tries to answer the question, “What do people feel about a certain topic?” by digging into opinions of many using a variety of automated tools. It is also known as opinion mining, subjectivity analysis, and appraisal extraction.

Sentiment analysis shares many characteristics and techniques with text mining. However, unlike text mining, which categorizes text by conceptual taxonomies of topics, sentiment classification generally deals with two classes (positive versus negative), a range of polarity (e.g., star ratings for movies), or a range in strength of opinion.

7. What are the common challenges with which sentiment analysis deals?