The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts, 1st ed. 2016 SpringerBriefs in Cognitive Computation Series, Vol. 4

In 6 chapters the book sheds light on the comparison of sentiment classification accuracy between single-word and multi-word concepts, for which a bespoke sentiment analysis system developed by the author was used.

1.   Introduction
1.1 Sentiment in Opinionated Text
1.2 Background
1.3 Research Problem

2.    Sentiment Analysis
2.1 Sentiment Analysis Challenges
2.2 Levels of Analysis
2.3 Supervised vs. Unsupervised Sentiment Analysis
2.4 Linguistics-based Sentiment Analysis
2.5 Lexicon-based Sentiment Analysis
2.6 Conclusion

4.   Unsupervised Sentiment Classification
4.1 Datasets
4.2 Classification Design and Implementation
4.2.1 Overview
4.2.2 Sentiment Classification Process
4.2.3 Polarity Value Thresholds
4.2.4 Implementation
4.3 Conclusion

5. Evaluation
5.1 Classification Performance
5.1.1 Research Question
5.1.2 Qualitative Differences Between the Datasets
5.1.3 SenticNet
5.1.4 Sentiment Analysis System
5.1.5 Sentiment Classification Design
5.2 Limitations
5.3 Conclusions

6.  Conclusion
6.1 Future Work
6.2 Final Remarks

 Index

Presents statistical analysis of SenticNet that has not been produced before

Offers original research into concept-level and knowledge-based sentiment analysis using the SenticNet sentiment lexicon

Broadens understanding of sentiment analysis

Includes supplementary material: sn.pub/extras