Author: Bing Liu
Publisher: Cambridge University Press
Release Date: 2015-06-04
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.
Author: Bing Liu
Publisher: Morgan & Claypool Publishers
Release Date: 2012
Genre: Language Arts & Disciplines
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis.Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations.This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography
Author: Federico Alberto Pozzi
Publisher: Morgan Kaufmann
Release Date: 2016-10-06
The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics
Author: Erik Cambria
Release Date: 2017-05-12
Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers’ sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well.
Author: Khurshid Ahmad
Publisher: Springer Science & Business Media
Release Date: 2011-08-24
This volume maps the watershed areas between two 'holy grails' of computer science: the identification and interpretation of affect – including sentiment and mood. The expression of sentiment and mood involves the use of metaphors, especially in emotive situations. Affect computing is rooted in hermeneutics, philosophy, political science and sociology, and is now a key area of research in computer science. The 24/7 news sites and blogs facilitate the expression and shaping of opinion locally and globally. Sentiment analysis, based on text and data mining, is being used in the looking at news and blogs for purposes as diverse as: brand management, film reviews, financial market analysis and prediction, homeland security. There are systems that learn how sentiments are articulated. This work draws on, and informs, research in fields as varied as artificial intelligence, especially reasoning and machine learning, corpus-based information extraction, linguistics, and psychology.
Sentiment analysis and opinion mining is a very popular and active research area in natural language processing, it deals with structured and unstructured data to identify and extract people's opinions, sentiments and emotions in many resources of subjectivity such as product reviews, blogs, social networks, etc. All existing feature-level opinion mining approaches deal with the detection of subjective sentences and eliminate objective ones before extracting explicit features and their related positive or negative polarities. However, objective sentences can carry implicit opinions and a lack attention given to such sentences can adversely affect the obtained results. In this paper, we propose a classification-based approach to extract implicit opinions from objective sentences. Firstly, we apply a rule-based approach to extract explicit feature-opinion pairs from subjective sentences. Secondly, in order to build a classification model, we construct a training corpus based on extracted explicit feature-opinion pairs and subjective sentences. Lastly, mining implicit feature-opinion pairs from objective sentences is formulated into a text classification problem using the model previously built. Tested on customer reviews in three different domains, experimental results show the effectiveness of mining opinions from objective sentences.
Author: Brian Clifton
Publisher: MITP-Verlags GmbH & Co. KG
Release Date: 2010
Mit dem kostenlosen Google Analytics können Sie herausfinden, wie Sie das Optimum aus Ihrer Website herausholen. Der Google-Insider und Web-Analytics-Experte Brian Clifton zeigt ausführlich, wie Sie Google Analytics gezielt und effektiv einsetzen. Durch die richtige Interpretation und Analyse Ihrer Daten erhalten Sie ein unverzichtbares Werkzeug, um Ihrer Website den letzten Schliff geben zu können und den Erfolg zu steigern.
Nachdem Luise Rinser zu Studienzwecken längere Zeit unter Leprakranken verbrachte, hat sie 1974 erstmals diese Dokumentation herausgegeben, um die Geißel der Menschheit zu analysieren und zur öffentlichen Diskussion zu bringen. „Dem Tode geweiht?“ sprengt den Rahmen eines Reports. Es ist Bericht und Erzählung einzelner Schicksale, Anklage und Wegweiser. Es wird über medizinische Informationen, Ansteckung, Erscheinungsformen und über Heilungsmöglichkeiten gesprochen. (Dieser Text bezieht sich auf eine frühere Ausgabe.)
This book presents a collection of research papers focusing on issues emerging from the interaction of information technologies and organizational systems. In particular, the individual contributions examine digital platforms and artifacts currently adopted in both the business world and society at large (people, communities, firms, governments, etc.). The topics covered include: virtual organizations, virtual communities, smart societies, smart cities, ecological sustainability, e-healthcare, e-government, and interactive policy-making (IPM). The book offers a multidisciplinary perspective on a variety of information systems topics. It is also particularly relevant to information systems practitioners such as IS managers, business managers and policy makers. The content is based on a selection of the best papers (original double-blind peer-reviewed contributions) presented at the annual conference of the Italian chapter of AIS, which was held in Milan, Italy in December 2013.
Author: Dr. Goutam Chakraborty
Publisher: SAS Institute
Release Date: 2014-11-22
Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program.
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
Author: Natalie Friedrich
Publisher: GRIN Verlag
Release Date: 2015-12-21
Genre: Business & Economics
Bachelor Thesis from the year 2015 in the subject Information Management, grade: 1,3, University of Dusseldorf "Heinrich Heine" (Institut für Sprache und Information), language: English, abstract: This work analyzes tweets linking to scientific papers to find out if the tweets are positive, or negative or do not express an opinion. This will inform the meaning of tweets as a measure of impact in the context of altmetrics. The following research questions are examined: - In how far can sentiment analysis be used to detect positive or negative statements towards scientific papers expressed on Twitter? - Do tweets linking to scientific papers express positive or negative opinions? How do sentiments differ by academic discipline? - How do results affect the meaning of tweets to scientific papers as an altmetric indicator?
Author: Wesley W. Chu
Publisher: Springer Science & Business Media
Release Date: 2013-09-24
The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.
Author: Andreas Meier
Release Date: 2009-02-17
Genre: Business & Economics
Die Autoren systematisieren die Internetnutzung durch Verwaltung und Politik anhand von Prozessbereichen wie eAssistance (Portale, Zugang für alle, Qualitätssicherung), eProcurement (elektronische Beschaffung, Public Offering) u. a. Neben den Grundlagen von eDemocracy und eGovernment stellen sie Fallstudien aus Verwaltungs- und Forschungsinstitutionen vor. Das Buch richtet sich an Studierende der Wirtschafts- und Politikwissenschaften sowie allgemein an Leser, die sich mit digitalen Partizipationsoptionen in der Wissensgesellschaft beschäftigen.