A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

Conclusion : on multiple levels of analysis, context, contingency and capital

In this concluding chapter, we bring together the threads and reflections on the chapters contained in this text and show how they relate to multi-level issues. The book has focused on the world of Human Resource Management (HRM) and the systems and practices it must put in place to foster innovation. Many of the contributions argue that in order to bring innovation about, organisations have to think carefully about the way in which they will integrate what is, in practice, organisationally relevant — but socially distributed — knowledge. They need to build a series of knowledge-intensive activities and networks, both within their own boundaries and across other important external inter-relationships. In so doing, they help to co-ordinate important information structures. They have, in effect, to find ways of enabling people to collaborate with each other at lower cost, by reducing both the costs of their co-ordination and the levels of unproductive search activity. They have to engineer these behaviours by reducing the risks for people that might be associated with incorrect ideas and help individuals, teams and business units to advance incomplete ideas that are so often difficult to codify. In short, a range of intangible assets must flow more rapidly throughout the organisation and an appropriate balance must be found between the rewards and incentives associated with creativity, novelty and innovation, versus the risks that innovation may also bring.

Recent advances in explaining hedge fund returns:implicit factors and exposures

We survey articles covering how hedge fund returns are explained, using largely non-linear multifactor models that examine the non-linear pay-offs and exposures of hedge funds. We provide an integrated view of the implicit factor and statistical factor models that are largely able to explain the hedge fund return-generating process. We present their evolution through time by discussing pioneering studies that made a significant contribution to knowledge, and also recent innovative studies that examine hedge fund exposures using advanced econometric methods. This is the first review that analyzes very recent studies that explain a large part of hedge fund variation. We conclude by presenting some gaps for future research.