Sentic Computing

Introduction

1.1 Sentic Computing   1.1.1 Motivations   1.1.2 Aims   1.1.3 Methodology

Background

2.1 Opinion Mining and Sentiment Analysis   2.1.1 The Buzz Mechanism   2.1.2 Origins and Peculiarities   2.1.3 Sub-Tasks 2.2 Main Approaches to Opinion Mining   2.2.1 From Heuristics to Discourse Structure   2.2.2 From Coarse to Fine Grained   2.2.3 From Keywords to Concepts 2.3 Towards Machines with Common Sense   2.3.1 The Importance of Common Sense   2.3.2 Knowledge Representation   2.3.3 From Logical Inference to Digital Intuition 2.4 Conclusions

Techniques

3.1 Affective Blending: Enabling Emotion-Sensitive Inference   3.1.1 AffectNet   3.1.2 AffectiveSpace 3.2 Affective Categorisation: Modelling Human Emotions   3.2.1 Categorical Versus Dimensional Approaches   3.2.2 The Hourglass of Emotions 3.3 Sentic Medoids: Clustering Affective Common Sense Concepts   3.3.1 Partitioning Around Medoids   3.3.2 Centroid Selection 3.4 Sentic Activation: A Two-Level Affective Reasoning Framework   3.4.1 Unconscious Reasoning   3.4.2 Conscious Reasoning 3.5 Sentic Panalogy: Switching Between Different Ways to Think   3.5.1 Changing Reasoning Strategies   3.5.2 Changing Reasoning Foci 3.6 Conclusions

Tools

4.1 SenticNet: A Semantic Resource for Opinion Mining   4.1.1 Building SenticNet   4.1.2 Working with SenticNet 4.2 Sentic Neural Networks: Brain-Inspired Affective Reasoning   4.2.1 Discrete Versus Continuous Approach   4.2.2 Affective Learning 4.3 Open Mind Common Sentics: An Emotion-Sensitive IUI   4.3.1 Games for Knowledge Acquisition   4.3.2 Collecting Affective Common Sense Knowledge 4.4 Isanette: A Common and Common Sense Knowledge Base   4.4.1 Probase   4.4.2 Building the Instance-Concept Matrix 4.5 Opinion Mining Engine: Structuring the Unstructured   4.5.1