E-Book, Englisch, 103 Seiten, eBook
Reihe: Socio-Affective Computing
ISBN: 978-3-319-25343-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)
Authors pay attention to the four main findings of the book :
-Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features.
- Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis.
- The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis.
- Semantic relations among the words in thetext have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Introduction.- Literature Survey.- Machine Learning Approach for Sentiment Analysis.- Semantic Parsing using Dependency Rules.- Sentiment Analysis using ConceptNet Ontology and Context Information.- Semantic Orientation based Approach for Sentiment Analysis.- Conclusions and FutureWork.- References.- Glossary.- Index.