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

The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.

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VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text

行銷硏究 : Marketing research

This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

The Oxford Handbook of Innovation

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The Theory of Industrial Organization

As cryptocoins are not tied to fundamental values or to investor protection regulation, their price dynamics is unhinged in both directions. In institutional asset management of conventional asset classes, target volatility concepts and dynamic allocation heuristics are popular to improve the robustness of portfolios. Can similar techniques also be used to construct delevered and diversified portfolios of crypto assets? A robust candidate approach for allocation is Hierarchical Risk Parity (HRP), as it incorporates a filtered correlation structure and is less sensitive to noise than quadratic optimization, as shown in several studies. Recent publications have extended the concept of HRP in several directions. We compare some of these extensions to determine which variant is most useful for constructing crypto baskets. We find that a particular type of adaptive HRP strategy outperforms other extensions on a risk-adjusted basis, leading us to a deeper investigation of the changing nature of correlation structures between cryptos - both quantitatively and visually. We find that structural breaks in crypto correlations are prevalent and that the best-fitting hierarchical cluster representations change over time, which is only captured by distance matrix-based adaptive HRP approaches.

Can Adaptive Seriational Risk Parity Tame Crypto Portfolios

This paper analyses the influence of in-house R&D and external knowledge acquisition on the total factor productivity (TFP) of listed Chinese firms for the time period 2001-2010. We find a quantitatively important positive effect of in-house R&D. The achieved level of technological sophistication of Chinese firms is sufficient to benefit from R&D collaboration with domestic partners. We do not find a significant effect for employing inventors with access to international knowledge or for collaborating with international partners. International knowledge acquisition is only effective if conducted via joint ventures, i.e. if it is supported by a deep organizational relationship.

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Philipp Sandner

Papers

Can Adaptive Seriational Risk Parity Tame Crypto Portfolios

In-house R&D and External Knowledge Acquisition – What Makes Chinese Firms Productive?