Main description:

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web.
Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.
 
Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:
•    Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference
•    Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text
•    Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses

This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.

Feature:

First approach to sentiment analysis that merges AI, linguistics, and psychology

Comprehensive explanation of popular sentic computing techniques

Full set of linguistic patterns for sentiment analysis

Downloadable knowledge base

Back cover:

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web.
Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:
•    Sentic Computing's multi-disciplinary approach to sentiment  analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference

•    Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text

•    Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses

This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain  and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.

Contents:

1. Introduction
1.1 Opinion Mining and Sentiment Analysis
1.2 Towards Machines with Common-Sense
1.3 Sentic Computing  

2. SenticNet
2.1 Knowledge Collection
2.2 Knowledge Representation
2.3 Knowledge-Based Reasoning  

3. Sentic Patterns
3.1 Semantic Parsing
3.2 Linguistic Rules
3.3 ELM Classifier  

4. Sentic Applications
4.1 Development of Social Web Systems
4.2 Development of HCI Systems
4.3 Development of E-Health Systems

5. Conclusion
5.1 Summary of Contributions
5.2 Limitations and Future Work