Semantic Sentiment Analysis In Social Streams
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Semantic Sentiment Analysis In Social Streams
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Author : Hassan Saif
language : en
Publisher:
Release Date : 2017
Semantic Sentiment Analysis In Social Streams written by Hassan Saif and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Semantic Sentiment Analysis In Social Streams
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Author : H. Saif
language : en
Publisher: IOS Press
Release Date : 2017-06-12
Semantic Sentiment Analysis In Social Streams written by H. Saif and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.
Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people’s opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis on social media, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment. However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. In order to address this problem, the author investigates the role of word semantics in sentiment analysis of microblogs. Specifically, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, the author proposes several approaches in this book for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words’ co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider word semantics for sentiment analysis at both entity and tweet levels, surpass non-semantic approaches in most evaluation scenarios. This book will be of interest to students, researchers and practitioners in the semantic sentiment analysis field.
Sentiment Analysis In Social Networks
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Author : Federico Alberto Pozzi
language : en
Publisher: Morgan Kaufmann
Release Date : 2016-10-06
Sentiment Analysis In Social Networks written by Federico Alberto Pozzi and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-06 with Computers categories.
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
Emerging Research Challenges And Opportunities In Computational Social Network Analysis And Mining
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Author : Nitin Agarwal
language : en
Publisher: Springer
Release Date : 2018-09-17
Emerging Research Challenges And Opportunities In Computational Social Network Analysis And Mining written by Nitin Agarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-17 with Social Science categories.
The contributors in this book share, exchange, and develop new concepts, ideas, principles, and methodologies in order to advance and deepen our understanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, behavioral science, computer science, psychology, cultural studies, information systems, operations research and communication to share, exchange, learn, and develop new concepts, ideas, principles, and methodologies. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining will be of interest to researchers, practitioners, and graduate students from the various disciplines listed above. The text facilitates the dissemination of investigations of the dynamics and structure of web based social networks. The book can be used as a reference text for advanced courses on Social Network Analysis, Sociology, Communication, Organization Theory, Cyber-anthropology, Cyber-diplomacy, and Information Technology and Justice.
Engineering Background Knowledge For Social Robots
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Author : L. Asprino
language : en
Publisher: IOS Press
Release Date : 2020-09-25
Engineering Background Knowledge For Social Robots written by L. Asprino and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Computers categories.
Social robots are embodied agents that perform knowledge-intensive tasks involving several kinds of information from different heterogeneous sources. This book, Engineering Background Knowledge for Social Robots, introduces a component-based architecture for supporting the knowledge-intensive tasks performed by social robots. The design was based on the requirements of a real socially-assistive robotic application, and all the components contribute to and benefit from the knowledge base which is its cornerstone. The knowledge base is structured by a set of interconnected and modularized ontologies which model the information, and is initially populated with linguistic, ontological and factual knowledge retrieved from Linked Open Data. Access to the knowledge base is guaranteed by Lizard, a tool providing software components, with an API for accessing facts stored in the knowledge base in a programmatic and object-oriented way. The author introduces two methods for engineering the knowledge needed by robots, a novel method for automatically integrating knowledge from heterogeneous sources with a frame-driven approach, and a novel empirical method for assessing foundational distinctions over Linked Open Data entities from a common-sense perspective. These effectively enable the evolution of the robot’s knowledge by automatically integrating information derived from heterogeneous sources and the generation of common-sense knowledge using Linked Open Data as an empirical basis. The feasibility and benefits of the architecture have been assessed through a prototype deployed in a real socially-assistive scenario, and the book presents two applications and the results of a qualitative and quantitative evaluation.
Identity Of Long Tail Entities In Text
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Author : F. Ilievski
language : en
Publisher: IOS Press
Release Date : 2019-11-29
Identity Of Long Tail Entities In Text written by F. Ilievski and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.
The digital era has generated a huge amount of data on the identities (profiles) of people, organizations and other entities in a digital format, largely consisting of textual documents such as news articles, encyclopedias, personal websites, books, and social media. Identity has thus been transformed from a philosophical to a societal issue, one requiring robust computational tools to determine entity identity in text. Computational systems developed to establish identity in text often struggle with long-tail cases. This book investigates how Natural Language Processing (NLP) techniques for establishing the identity of long-tail entities – which are all infrequent in communication, hardly represented in knowledge bases, and potentially very ambiguous – can be improved through the use of background knowledge. Topics covered include: distinguishing tail entities from head entities; assessing whether current evaluation datasets and metrics are representative for long-tail cases; improving evaluation of long-tail cases; accessing and enriching knowledge on long-tail entities in the Linked Open Data cloud; and investigating the added value of background knowledge (“profiling”) models for establishing the identity of NIL entities. Providing novel insights into an under-explored and difficult NLP challenge, the book will be of interest to all those working in the field of entity identification in text.
Emotions And Personality In Personalized Services
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Author : Marko Tkalčič
language : en
Publisher: Springer
Release Date : 2016-07-13
Emotions And Personality In Personalized Services written by Marko Tkalčič and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-13 with Computers categories.
Personalization is ubiquitous from search engines to online-shopping websites helping us find content more efficiently and this book focuses on the key developments that are shaping our daily online experiences. With advances in the detection of end users’ emotions, personality, sentiment and social signals, researchers and practitioners now have the tools to build a new generation of personalized systems that will really understand the user’s state and deliver the right content. With leading experts from a vast array of domains from user modeling, mobile sensing and information retrieval to artificial intelligence, human-computer interaction (HCI) social computing and psychology, a broad spectrum of topics are covered. From discussing psychological theoretical models and exploring state-of-the-art methods for acquiring emotions and personality in an unobtrusive way, as well as describing how these concepts can be used to improve various aspects of the personalization process and chapters that discuss evaluation and privacy issues. Emotions and Personality in Personalized Systems will help aid researchers and practitioners develop and evaluate user-centric personalization systems that take into account the factors that have a tremendous impact on our decision-making – emotions and personality.
Study On Data Placement Strategies In Distributed Rdf Stores
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Author : D.D. Janke
language : en
Publisher: IOS Press
Release Date : 2020-03-18
Study On Data Placement Strategies In Distributed Rdf Stores written by D.D. Janke and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-18 with Computers categories.
The distributed setting of RDF stores in the cloud poses many challenges, including how to optimize data placement on the compute nodes to improve query performance. In this book, a novel benchmarking methodology is developed for data placement strategies; one that overcomes these limitations by using a data-placement-strategy-independent distributed RDF store to analyze the effect of the data placement strategies on query performance. Frequently used data placement strategies have been evaluated, and this evaluation challenges the commonly held belief that data placement strategies which emphasize local computation lead to faster query executions. Indeed, results indicate that queries with a high workload can be executed faster on hash-based data placement strategies than on, for example, minimal edge-cut covers. The analysis of additional measurements indicates that vertical parallelization (i.e., a well-distributed workload) may be more important than horizontal containment (i.e., minimal data transport) for efficient query processing. Two such data placement strategies are proposed: the first, found in the literature, is entitled overpartitioned minimal edge-cut cover, and the second is the newly developed molecule hash cover. Evaluation revealed a balanced query workload and a high horizontal containment, which lead to a high vertical parallelization. As a result, these strategies demonstrated better query performance than other frequently used data placement strategies. The book also tests the hypothesis that collocating small connected triple sets on the same compute node while balancing the amount of triples stored on the different compute nodes leads to a high vertical parallelization.
Advances In Ontology Design And Patterns
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Author : K. Hammar
language : en
Publisher: IOS Press
Release Date : 2017-12-27
Advances In Ontology Design And Patterns written by K. Hammar and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-27 with Computers categories.
The study of patterns in the context of ontology engineering for the semantic web was pioneered more than a decade ago by Blomqvist, Sandkuhl and Gangemi. Since then, this line of research has flourished and led to the development of ontology design patterns, knowledge patterns, and linked data patterns: the patterns as they are known by ontology designers, knowledge engineers, and linked data publishers, respectively. A key characteristic of those patterns is that they are modular and reusable solutions to recurrent problems in ontology engineering and linked data publishing. This book contains recent contributions which advance the state of the art on theory and use of ontology design patterns. The papers collected in this book cover a range of topics, from a method to instantiate content patterns, a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations.
Query Processing Over Graph Structured Data On The Web
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Author : M. Acosta Deibe
language : en
Publisher: IOS Press
Release Date : 2018-10-12
Query Processing Over Graph Structured Data On The Web written by M. Acosta Deibe and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-12 with Computers categories.
In the last years, Linked Data initiatives have encouraged the publication of large graph-structured datasets using the Resource Description Framework (RDF). Due to the constant growth of RDF data on the web, more flexible data management infrastructures must be able to efficiently and effectively exploit the vast amount of knowledge accessible on the web. This book presents flexible query processing strategies over RDF graphs on the web using the SPARQL query language. In this work, we show how query engines can change plans on-the-fly with adaptive techniques to cope with unpredictable conditions and to reduce execution time. Furthermore, this work investigates the application of crowdsourcing in query processing, where engines are able to contact humans to enhance the quality of query answers. The theoretical and empirical results presented in this book indicate that flexible techniques allow for querying RDF data sources efficiently and effectively.