Prominent Feature Extraction for Sentiment Analysis 2016 Edition Contributor(s): Agarwal, Basant (Author), Mittal, Namita (Author) |
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ISBN: 3319253417 ISBN-13: 9783319253411 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: December 2015 |
Additional Information |
BISAC Categories: - Medical | Neuroscience - Computers | Databases - Data Mining - Computers | Document Management |
Dewey: 004 |
Series: Socio-Affective Computing |
Physical Information: 0.31" H x 6.14" W x 9.21" (0.78 lbs) 103 pages |
Descriptions, Reviews, Etc. |
Publisher Description: The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book: |