| 书目名称 | Prominent Feature Extraction for Sentiment Analysis |
| 编辑 | Basant Agarwal,Namita Mittal |
| 视频video | |
| 概述 | Includes a novel semantic parsing scheme which may be applied to many Natural language processing tasks.Provides an efficient machine learning approach for sentiment analysis.Easy to understand and de |
| 丛书名称 | Socio-Affective Computing |
| 图书封面 |  |
| 描述 | .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 :. -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 per |
| 出版日期 | Book 2016 |
| 关键词 | Machine Learning; Minimum Redundancy and Maximum Relevance feature selection; Prominent Feature Extrac |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-25343-5 |
| isbn_softcover | 978-3-319-79775-5 |
| isbn_ebook | 978-3-319-25343-5Series ISSN 2509-5706 Series E-ISSN 2509-5714 |
| issn_series | 2509-5706 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 18:41:42
