The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts

The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts

by Raoul Biagioni

Paperback(1st ed. 2016)

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Overview

The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts by Raoul Biagioni


The research and its outcomes presented in this book, is about lexicon-based sentiment analysis. It uses single-, and multi-word concepts from the SenticNet sentiment lexicon as the source of sentiment information for the purpose of sentiment classification.

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.



This book will be of interest to students, educators and researchers in the field of Sentic Computing.

Product Details

ISBN-13: 9783319389707
Publisher: Springer International Publishing
Publication date: 05/28/2016
Series: SpringerBriefs in Cognitive Computation , #4
Edition description: 1st ed. 2016
Pages: 55
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

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

3. SenticNet
3.1 The Common Sense Nature of SenticNet Knowledge
3.2 A Seminal Approach to Concept-based Sentiment Analysis
3.3 Producing SenticNet
3.4 SenticNet Processes
3.5 SenticNet Knowledge: Encoding
3.6 SenticNet Access Methods
3.7 SenticNet in Numbers
3.7.1 Concept Types: Number of Words
3.7.2 Analysis of Polarity Values: Single-Word vs. Multi-Word Concepts
3.8 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

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