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Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned.

Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases.

Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.



Chapter 1. An Introduction to Text Mining

The problem of text mining has gained increasing attention in recent years because of the large amounts of text data, which are created in a variety of social network, web, and other information-centric applications. Unstructured data is the easiest form of data which can be created in any application scenario. As a result, there has been a tremendous need to design methods and algorithms which can effectively process a wide variety of text applications. This book will provide an overview of the different methods and algorithms which are common in the text domain, with a particular focus on mining methods.
Charu C. Aggarwal, ChengXiang Zhai

Chapter 2. Information Extraction from Text

Information extraction is the task of finding structured information from unstructured or semi-structured text. It is an important task in text mining and has been extensively studied in various research communities including natural language processing, information retrieval and Web mining. It has a wide range of applications in domains such as biomedical literature mining and business intelligence. Two fundamental tasks of information extraction are named entity recognition and relation extraction. The former refers to finding names of entities such as people, organizations and locations. The latter refers to finding the semantic relations such as FounderOf and HeadquarteredIn between entities. In this chapter we provide a survey of the major work on named entity recognition and relation extraction in the past few decades, with a focus on work from the natural language processing community.
Jing Jiang

Chapter 3. A Survey of Text Summarization Techniques

Numerous approaches for identifying important content for automatic text summarization have been developed to date. Topic representation approaches first derive an intermediate representation of the text that captures the topics discussed in the input. Based on these representations of topics, sentences in the input document are scored for importance. In contrast, in indicator representation approaches, the text is represented by a diverse set of possible indicators of importance which do not aim at discovering topicality. These indicators are combined, very often using machine learning techniques, to score the importance of each sentence. Finally, a summary is produced by selecting sentences in a greedy approach, choosing the sentences that will go in the summary one by one, or globally optimizing the selection, choosing the best set of sentences to form a summary. In this chapter we give a broad overview of existing approaches based on these distinctions, with particular attention on how representation, sentence scoring or summary selection strategies alter the overall performance of the summarizer. We also point out some of the peculiarities of the task of summarization which have posed challenges to machine learning approaches for the problem, and some of the suggested solutions.
Ani Nenkova, Kathleen McKeown

Chapter 4. A Survey of Text Clustering Algorithms

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.
Charu C. Aggarwal, ChengXiang Zhai

Chapter 5. Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond

The bag-of-words representation commonly used in text analysis can be analyzed very efficiently and retains a great deal of useful information, but it is also troublesome because the same thought can be expressed using many different terms or one term can have very different meanings. Dimension reduction can collapse together terms that have the same semantics, to identify and disambiguate terms with multiple meanings and to provide a lower-dimensional representation of documents that reflects concepts instead of raw terms. In this chapter, we survey two influential forms of dimension reduction. Latent semantic indexing uses spectral decomposition to identify a lower-dimensional representation that maintains semantic properties of the documents. Topic modeling, including probabilistic latent semantic indexing and latent Dirichlet allocation, is a form of dimension reduction that uses a probabilistic model to find the co-occurrence patterns of terms that correspond to semantic topics in a collection of documents. We describe the basic technologies in detail and expose the underlying mechanism. We also discuss recent advances that have made it possible to apply these techniques to very large and evolving text collections and to incorporate network structure or other contextual information.
Steven P. Crain, Ke Zhou, Shuang-Hong Yang, Hongyuan Zha

Chapter 6. A Survey of Text Classification Algorithms

The problem of classification has been widely studied in the data mining, machine learning, database, and information retrieval communities with applications in a number of diverse domains, such as target marketing, medical diagnosis, news group filtering, and document organization. In this paper we will provide a survey of a wide variety of text classification algorithms.
Charu C. Aggarwal, ChengXiang Zhai

Chapter 7. Transfer Learning for Text Mining

Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem. Various transfer learning methods are designed to extract the useful knowledge from different but related auxiliary domains. In its connection to text mining, transfer learning has found novel and useful applications. In this chapter, we will review some most recent developments in transfer learning for text mining, explain related algorithms in detail, and project future developments of this field. We focus on two important topics: cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images.
Weike Pan, Erheng Zhong, Qiang Yang

Chapter 8. Probabilistic Models for Text Mining

A number of probabilistic methods such as LDA, hidden Markov models, Markov random fields have arisen in recent years for probabilistic analysis of text data. This chapter provides an overview of a variety of probabilistic models for text mining. The chapter focuses more on the fundamental probabilistic techniques, and also covers their various applications to different text mining problems. Some examples of such applications include topic modeling, language modeling, document classification, document clustering, and information extraction.
Yizhou Sun, Hongbo Deng, Jiawei Han

Chapter 9. Mining Text Streams

The large amount of text data which are continuously produced over time in a variety of large scale applications such as social networks results in massive streams of data. Typically massive text streams are created by very large scale interactions of individuals, or by structured creations of particular kinds of content by dedicated organizations. An example in the latter category would be the massive text streams created by news-wire services. Such text streams provide unprecedented challenges to data mining algorithms from an efficiency perspective. In this chapter, we review text stream mining algorithms for a wide variety of problems in data mining such as clustering, classification and topic modeling. We also discuss a number of future challenges in this area of research.
Charu C. Aggarwal

Chapter 10. Translingual Mining from Text Data

Like full-text translation, cross-language information retrieval (CLIR) is a task that requires some form of knowledge transfer across languages. Although robust translation resources are critical for constructing high quality translation tools, manually constructed resources are limited both in their coverage and in their adaptability to a wide range of applications. Automatic mining of translingual knowledge makes it possible to complement hand-curated resources. This chapter describes a growing body of work that seeks to mine translingual knowledge from text data, in particular, data found on the Web. We review a number of mining and filtering strategies, and consider them in the context of statistical machine translation, showing that these techniques can be effective in collecting large quantities of translingual knowledge necessary for CLIR.
Jian-Yun Nie, Jianfeng Gao, Guihong Cao

Chapter 11. Text Mining in Multimedia

A large amount of multimedia data (e.g., image and video) is now available on the Web. A multimedia entity does not appear in isolation, but is accompanied by various forms of metadata, such as surrounding text, user tags, ratings, and comments etc. Mining these textual metadata has been found to be effective in facilitating multimedia information processing and management. A wealth of research efforts has been dedicated to text mining in multimedia. This chapter provides a comprehensive survey of recent research efforts. Specifically, the survey focuses on four aspects: (a) surrounding text mining; (b) tag mining; (c) joint text and visual content mining; and (d) cross text and visual content mining. Furthermore, open research issues are identified based on the current research efforts.
Zheng-Jun Zha, Meng Wang, Jialie Shen, Tat-Seng Chua

Chapter 12. Text Analytics in Social Media

The rapid growth of online social media in the form of collaborativelycreated content presents new opportunities and challenges to both producers and consumers of information. With the large amount of data produced by various social media services, text analytics provides an effective way to meet usres’ diverse information needs. In this chapter, we first introduce the background of traditional text analytics and the distinct aspects of textual data in social media. We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world examples.
Xia Hu, Huan Liu

Chapter 13. A Survey of Opinion Mining and Sentiment Analysis

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions about their products and services. Potential customers also want to know the opinions of existing users before they use a service or purchase a product.
With the explosive growth of social media (i.e., reviews, forum discussions, blogs and social networks) on the Web, individuals and organizations are increasingly using public opinions in these media for their decision making. However, finding and monitoring opinion sites on the Web and distilling the information contained in them remains a formidable task because of the proliferation of diverse sites. Each site typically contains a huge volume of opinionated text that is not always easily deciphered in long forum postings and blogs. The average human reader will have difficulty identifying relevant sites and accurately summarizing the information and opinions contained in them. Moreover, it is also known that human analysis of text information is subject to considerable biases, e.g., people often pay greater attention to opinions that are consistent with their own preferences. People also have difficulty, owing to their mental and physical limitations, producing consistent results when the amount of information to be processed is large. Automated opinion mining and summarization systems are thus needed, as subjective biases and mental limitations can be overcome with an objective sentiment analysis system.
In the past decade, a considerable amount of research has been done in academia [58,76]. There are also numerous commercial companies that provide opinion mining services. In this chapter, we first define the opinion mining problem. From the definition, we will see the key technical issues that need to be addressed. We then describe various key mining tasks that have been studied in the research literature and their representative techniques. After that, we discuss the issue of detecting opinion spam or fake reviews. Finally, we also introduce the research topic of assessing the utility or quality of online reviews.
Bing Liu, Lei Zhang

Chapter 14. Biomedical Text Mining: A Survey of Recent Progress

The biomedical community makes extensive use of text mining technology. In the past several years, enormous progress has been made in developing tools and methods, and the community has been witness to some exciting developments. Although the state of the community is regularly reviewed, the sheer volume of work related to biomedical text mining and the rapid pace in which progress continues to be made make this a worthwhile, if not necessary, endeavor. This chapter provides a brief overview of the current state of text mining in the biomedical domain. Emphasis is placed on the resources and tools available to biomedical researchers and practitioners, as well as the major text mining tasks of interest to the community. These tasks include the recognition of explicit facts from biomedical literature, the discovery of previously unknown or implicit facts, document summarization, and question answering. For each topic, its basic challenges and methods are outlined and recent and influential work is reviewed.
Matthew S. Simpson, Dina Demner-Fushman


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