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Author: H. Saif
Publisher: IOS Press
ISBN: 1614997519
Category : Computers
Languages : en
Pages : 310
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Book Description
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.
Author: H. Saif
Publisher: IOS Press
ISBN: 1614997519
Category : Computers
Languages : en
Pages : 310
Get Book Here
Book Description
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.
Author: Hassan Saif
Publisher:
ISBN: 9783898387262
Category :
Languages : en
Pages : 286
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Book Description
Author: Federico Alberto Pozzi
Publisher: Morgan Kaufmann
ISBN: 0128044381
Category : Computers
Languages : en
Pages : 286
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Book Description
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: K. Hammar
Publisher: IOS Press
ISBN: 1614998264
Category : Computers
Languages : en
Pages : 162
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Book Description
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.
Author: M. Acosta Deibe
Publisher: IOS Press
ISBN: 1614999163
Category : Computers
Languages : en
Pages : 244
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Book Description
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.
Author: B. Yan
Publisher: IOS Press
ISBN: 1614999899
Category : Computers
Languages : en
Pages : 170
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Book Description
Geographic knowledge graphs can have an important role in delivering interoperability, accessibility and the demands of conceptualization in geographic information science (GIS). However, the massive amount of accompanying information and the enormous diversity of geographic knowledge graphs limits their applicability and hinders the widespread adoption of this useful structured knowledge. This book, Geographic Knowledge Graph Summarization, focuses on the ways in which geographic knowledge graphs can be digested and summarized. Such a summarization would relieve the burden of information overload for end users and reduce data storage, as well as speeding up queries and eliminating ‘noise’. The book introduces the general concept of geospatial inductive bias and explains the different ways in which this idea can be used in the summarization of geographic knowledge graphs. The book breaks up the task of summarization into separate but related components, and after an introduction and a brief overview of concepts and theories, Chapters 3, 4 and 5 explore hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. Chapter 6 presents a spatial knowledge map interface which illustrates the effectiveness of summarization. The book integrates top-down knowledge engineering and bottom-up knowledge learning methods, and will do much to promote awareness of this fascinating area and related issues.
Author: S. Thoma
Publisher: IOS Press
ISBN: 1643680293
Category : Computers
Languages : en
Pages : 174
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Book Description
Many web pages include structured data in the form of semantic markup, which can be transferred to the Resource Description Framework (RDF) or provide an interface to retrieve RDF data directly. This RDF data enables machines to automatically process and use the data. When applications need data from more than one source the data has to be integrated, and the automation of this can be challenging. Usually, vocabularies are used to concisely describe the data, but because of the decentralized nature of the web, multiple data sources can provide similar information with different vocabularies, making integration more difficult. This book, Multi-modal Data Fusion based on Embeddings, describes how similar statements about entities can be identified across sources, independent of the vocabulary and data modeling choices. Previous approaches have relied on clean and extensively modeled ontologies for the alignment of statements, but the often noisy data in a web context does not necessarily adhere to these prerequisites. In this book, the use of RDF label information of entities is proposed to tackle this problem. In combination with embeddings, the use of label information allows for a better integration of noisy data, something that has been empirically confirmed by experiment. The book presents two main scientific contributions: the vocabulary and modeling agnostic fusion approach on the purely textual label information, and the combination of three different modalities into one multi-modal embedding space for a more human-like notion of similarity. The book will be of interest to all those faced with the problem of processing data from multiple web-based sources.
Author: F. Darari
Publisher: IOS Press
ISBN: 1643680358
Category : Computers
Languages : en
Pages : 194
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Book Description
The increasing amount of structured data available on the Web is laying the foundations for a global-scale knowledge base. But the ever increasing amount of Semantic Web data gives rise to the question – how complete is that data? Though data on the Semantic Web is generally incomplete, some may indeed be complete. In this book, the author deals with how to manage and consume completeness information about Semantic Web data. In particular, the book explores how completeness information can guarantee the completeness of query answering. Optimization techniques for completeness reasoning and the conducting of experimental evaluations are provided to show the feasibility of the approaches, as well as a technique for checking the soundness of queries with negation via reduction to query completeness checking. Other topics covered include completeness information with timestamps, and two demonstrators – CORNER and COOL-WD – are provided to show how a completeness framework can be realized. Finally, the book investigates an automated method to generate completeness statements from text on the Web. The book will be of interest to anyone whose work involves dealing with Web-data completeness.
Author: P. Vougiouklis
Publisher: IOS Press
ISBN: 1643680676
Category : Computers
Languages : en
Pages : 174
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Book Description
Most people need textual or visual interfaces to help them make sense of Semantic Web data. In this book, the author investigates the problems associated with generating natural language summaries for structured data encoded as triples using deep neural networks. An end-to-end trainable architecture is proposed, which encodes the information from a set of knowledge graph triples into a vector of fixed dimensionality, and generates a textual summary by conditioning the output on this encoded vector. Different methodologies for building the required data-to-text corpora are explored to train and evaluate the performance of the approach. Attention is first focused on generating biographies, and the author demonstrates that the technique is capable of scaling to domains with larger and more challenging vocabularies. The applicability of the technique for the generation of open-domain Wikipedia summaries in Arabic and Esperanto – two under-resourced languages – is then discussed, and a set of community studies, devised to measure the usability of the automatically generated content by Wikipedia readers and editors, is described. Finally, the book explains an extension of the original model with a pointer mechanism that enables it to learn to verbalise in a different number of ways the content from the triples while retaining the capacity to generate words from a fixed target vocabulary. The evaluation of performance using a dataset encompassing all of English Wikipedia is described, with results from both automatic and human evaluation both of which highlight the superiority of the latter approach as compared to the original architecture.
Author: D.D. Janke
Publisher: IOS Press
ISBN: 1643680692
Category : Computers
Languages : en
Pages : 312
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Book Description
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.
