Prominent Feature Extraction for Sentiment Analysis [recurso electrónico] / by Basant Agarwal, Namita Mittal.

Por: Agarwal, Basant [author.]Colaborador(es): Mittal, Namita [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Socio-Affective ComputingEditor: Cham : Springer International Publishing : Imprint: Springer, 2016Descripción: XIX, 103 p. 10 illus., 2 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319253435Tema(s): Medicine | Neurosciences | Data mining | Text processing (Computer science) | Application software | Computational linguistics | Biomedicine | Neurosciences | Document Preparation and Text Processing | Computational Linguistics | Data Mining and Knowledge Discovery | Information Systems Applications (incl. Internet) | Computer Appl. in Social and Behavioral SciencesFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 612.8 Clasificación LoC:RC321-580Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer eBooksResumen: 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 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 the text 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.
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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.

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 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 the text 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.

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