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.

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Sentiment Analysis and Opinion Mining

is one of the most recognizablecharacters in 20th century cinema. HAL is an artiﬁcial agent capable of such advancedlanguage behavior as speaking and understanding English, and at a crucial moment inthe plot, even reading lips. It is now clear that HAL’s creator, Arthur C. Clarke, wasa little optimistic in predicting when an artiﬁcial agent such as HAL would be avail-able. But just how far off was he? What would it take to create at least the language-relatedpartsofHAL?WecallprogramslikeHALthatconversewithhumansinnatural

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Speech and Language Processing

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

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From frequency to meaning: vector space models of semantics

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0.56 to 0.75 for individual words and from r = 0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

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Computing semantic relatedness using Wikipedia-based explicit semantic analysis

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.

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Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

The emergence of total quality management and the ISO 9000 suite of standards has allowed a re-think of how (and why) the postimplementation evaluation of computer systems is to be carried out. Traditional performance measurement, modeling and analysis techniques - while not discredited - have been tempered with a more holistic ideology. This article recommends a sociotechnical approach to determining the quality of a computer information system. In this context, two postulates have been proposed and tested by field survey of expert systems in the insurance industry in North America. Postulate one focuses on a multidimensional concept of IS quality comprising the characteristics of task, technology, people and organization. Postulate two deals with differences in assessments of these characteristics according to stakeholder groups: managers, developers, and users. Summarizes the key findings of these postulates in the context of the TQM and ISO 9000 philosophies.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2285861_code1030419.pdf?abstractid=2285861&mirid=4

A Socio-Technical Framework for Quality Assessment of Computer Information Systems

The emergence of total quality management and the ISO 9000 suite of standards has allowed a re‐think of how (and why) the post‐implementation evaluation of computer systems is to be carried out Traditional performance measurement, modeling and analysis techniques – while not discredited – have been tempered with a more holistic ideology This article recommends a socio‐technical approach to determining the quality of a computer information system In this context, two postulates have been proposed and tested by field survey of expert systems in the insurance industry in North America Postulate one focuses on a multidimensional concept of IS quality comprising the characteristics of task, technology, people and organization Postulate two deals with differences in assessments of these characteristics according to stakeholder groups: managers, developers, and users Summarizes the key findings of these postulates in the context of the TQM and ISO 9000 philosophies

A socio‐technical framework for quality assessment of computer information systems

In this article we discuss the problem of evaluating the quality of expert systems. The solution we propose is a diagnostic instrument that assesses the relative strengths and weaknesses of an operational expert system. Such a tool is designed to identify the characteristics and features that determine the system's quality (or lack of it) as a means of supporting the knowledge engineering process. Our approach is based on underlying concepts form socio-technical theory. It is also empirically subjected to a rigorous validation methodology.

David W. Conrath

Papers

Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

A Socio-Technical Framework for Quality Assessment of Computer Information Systems

A socio‐technical framework for quality assessment of computer information systems

Toward a diagnostic instrument for assessing the quality of expert systems