LEADER 05158nam 22007575 450 001 9910253872903321 005 20200705061614.0 010 $a3-319-25343-3 024 7 $a10.1007/978-3-319-25343-5 035 $a(CKB)3710000000539293 035 $a(EBL)4199810 035 $a(SSID)ssj0001596896 035 $a(PQKBManifestationID)16297040 035 $a(PQKBTitleCode)TC0001596896 035 $a(PQKBWorkID)14885725 035 $a(PQKB)10645022 035 $a(DE-He213)978-3-319-25343-5 035 $a(MiAaPQ)EBC4199810 035 $a(PPN)19088438X 035 $a(EXLCZ)993710000000539293 100 $a20151214d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProminent Feature Extraction for Sentiment Analysis$b[electronic resource] /$fby Basant Agarwal, Namita Mittal 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (118 p.) 225 1 $aSocio-Affective Computing,$x2509-5706 300 $aDescription based upon print version of record. 311 $a3-319-25341-7 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- 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. 330 $aThe objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. -Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis. 410 0$aSocio-Affective Computing,$x2509-5706 606 $aNeurosciences 606 $aNatural language processing (Computer science) 606 $aComputational linguistics 606 $aData mining 606 $aApplication software 606 $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aComputer Appl. in Social and Behavioral Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/I23028 615 0$aNeurosciences. 615 0$aNatural language processing (Computer science). 615 0$aComputational linguistics. 615 0$aData mining. 615 0$aApplication software. 615 14$aNeurosciences. 615 24$aNatural Language Processing (NLP). 615 24$aComputational Linguistics. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aComputer Appl. in Social and Behavioral Sciences. 676 $a610 700 $aAgarwal$b Basant$4aut$4http://id.loc.gov/vocabulary/relators/aut$01062677 702 $aMittal$b Namita$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910253872903321 996 $aProminent Feature Extraction for Sentiment Analysis$92527564 997 $aUNINA