Sentic Computing [electronic resource] : Techniques, Tools, and Applications /
Cambria, Erik.author.
Hussain, Amir.author.
In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
Description based upon print version of record.
Sentic Computing; Foreword; Preface; Acknowledgments; Contents; Acronyms; 1 Introduction; 1.1 Sentic Computing; 1.1.1 Motivations; 1.1.2 Aims; 1.1.3 Methodology; References; 2 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 Intuition2.4 Conclusions; References; 3 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; 3.3.1 Partitioning Around Medoids; 3.3.2 Centroid Selection; 3.4 Sentic Activation: Two-Level Affective Reasoning; 3.4.1 Unconscious Reasoning; 3.4.2 Conscious Reasoning
3.5 Sentic Panalogy: Switching Between Different Ways to Think3.5.1 Changing Reasoning Strategies; 3.5.2 Changing Reasoning Foci; 3.6 Conclusions; References; 4 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 Base4.4.1 Probase; 4.4.2 Building the Instance-Concept Matrix; 4.5 Opinion Mining Engine: Structuring the Unstructured; 4.5.1 Constitutive Modules; 4.5.2 Evaluation; 4.6 Conclusions; References; 5 Applications; 5.1 Development of Social Web Systems; 5.1.1 Troll Filtering; 5.1.2 Social Media Marketing; 5.1.3 Sentic Album; 5.2 Development of HCI Systems; 5.2.1 Sentic Avatar; 5.2.2 Sentic Chat; 5.2.3 Sentic Corner; 5.3 Development of E-Health Systems; 5.3.1 Crowd Validation; 5.3.2 Sentic PROMs; 5.4 Conclusions; References; 6 Concluding Remarks
6.1 Summary of Contributions6.1.1 Techniques; 6.1.2 Tools; 6.1.3 Applications; 6.2 Limitations and Future Work; 6.2.1 Limitations; 6.2.2 Future Work; 6.3 Conclusions; References
Includes bibliographical references.
In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
Medicine.
Data mining.
Mathematics.
Linguistics.
Cognitive psychology.
Biomedicine, general.
Data Mining and Knowledge Discovery.
Mathematics, general.
Linguistics, general.
Cognitive Psychology.