Relationships between web analytics text mining and sentiment analysis

CHAPTER 7 Text Mining, Sentiment Analysis, and Social Analytics LEARNING OBJECTIVES 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 ■■ Learn three facets of Web analytics—content, structure, and usage mining ■■ Know social analytics including social media and social network analyses ■■ ■■ T his 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. 7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales 389 7.2 Text Analytics and Text Mining Overview 392 7.3 Natural Language Processing (NLP) 397 7.4 Text Mining Applications 402 7.5 Text Mining Process 410 7.6 Sentiment Analysis 418 388 Chapter 7 • Text Mining, Sentiment Analysis, and Social Analytics 389 7.7 7.8 7.9 7.10 Web Mining Overview 429 Search Engines 433 Web Usage Mining 441 Social Analytics 446 7.1 OPENING VIGNETTE: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales BACKGROUND Amadori Group, or Gruppo Amadori in Italian, is a leading manufacturing company in Italy that produces and markets food products. Headquartered in San Vittore di Cesena, Italy, the company employs more than 7,000 people and operates 16 production plants. Amadori wanted to evolve its marketing to dynamically align with the changing lifestyles and dietary needs of young people aged 25-35. It sought to create fun ways to engage this target segment by exploiting the potential of online marketing and social media. The company wanted to boost brand visibility, encourage customer loyalty, and gauge consumers’ reactions to products and marketing campaigns. ENGAGING YOUNG ADULTS WITH CREATIVE DIGITAL MARKETING PROMOTIONS Together with Tecla (a digital business company), Amadori used IBM WebSphere® Portal and IBM Web Content Manager software to create and manage interactive content for four mini websites, or “minisites,” which promote ready-to-eat and quick-to-prepare products that fit young adults’ preferences and lifestyles. For example, to market its new Evviva sausage product, the company created the “Evviva Il Würstel Italiano” minisite and let consumers upload images and videos of themselves attending events organized by Amadori. To encourage participation, the company offered the winner a spot in its next national ad campaign. With this and other campaigns, the Amadori marketing staff compiled a database of consumer profiles by asking minisite visitors to share data to enter competitions,