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001			85099
003			ES-MaUEC
005			20240611040141.0
007			cr nn 008mamaa
008			151214s2016 gw | s |||| 0|eng d
020			_a9783319253435
040			_aES-MaUEC _bspa
050		4	_aQA76.5913 _b.A43 2016 EB
082	0	4	_a612.8
100	1		_aAgarwal, Basant _0Local _1http://viaf.org/viaf/2146285385315370868 _997805
245	1	0	_aProminent Feature Extraction for Sentiment Analysis _cby Basant Agarwal, Namita Mittal
250			_a1st ed.
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XIX, 103 páginas) _b10 ilustraciones, 2 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aSocio-Affective Computing
505	0		_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.
520			_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.
650		7	_aSemántica _xProceso de datos _9175270 _2embne
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aMittal, Namita. _997806 _0Local _1http://viaf.org/viaf/23146285361415371545
710	2		_aSpringerLink (Online service) _0Local _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-25343-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319253435
907			_a.b12943770 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11613233 _z30-06-17
988	0	0	_aEBOOK, EBSPRINGER, GOBI_sep2018
988			_aEbook_one2one
998			_aSI _a_alco _a_vill _b10/2018 _cm _dz _ek _feng _ggw _h0
999			_c85099 _d85099 _x1