Prominent Feature Extraction for Sentiment Analysis

Name: Prominent Feature Extraction for Sentiment Analysis
Brand: Springer International Publishing AG
SKU: 9783319253411
Price: 39286 HUF
Availability: InStock

Szerző: Basant Agarwal, Namita Mittal

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer International Publishing AG

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

39 286 Ft

The objective of this monograph is to improve the performance of the sentiment analysis model by inc...

Információk a könyvről

Szerző

Basant Agarwal, Namita Mittal

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2015

oldal

103

EAN

9783319253411

ISBN

3319253417

Enbook ID

09535545

Kiadó

Springer International Publishing AG

Súly

387

Méretek

155 x 235 x 11

Teljes leírás

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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Prominent Feature Extraction for Sentiment Analysis

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