LEADER 00961oam 2200337zu 450 001 996201311403316 005 20210807003658.0 010 $a1-5090-7068-0 010 $a0-7695-3812-6 035 $a(CKB)2400000000003013 035 $a(SSID)ssj0000452306 035 $a(PQKBManifestationID)12148611 035 $a(PQKBTitleCode)TC0000452306 035 $a(PQKBWorkID)10468533 035 $a(PQKB)10638872 035 $a(EXLCZ)992400000000003013 100 $a20160829d2009 uy 101 0 $aeng 181 $ctxt 182 $cc 183 $acr 200 10$a2009 World Conference on Services Part II 210 31$a[Place of publication not identified]$cIEEE$d2009 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4244-5303-8 702 $aIEEE Staff 801 0$bPQKB 906 $aPROCEEDING 912 $a996201311403316 996 $a2009 World Conference on Services Part II$92502475 997 $aUNISA LEADER 01350nam a2200277 i 4500 001 991003575949707536 008 180911s2016 cau 001 0 eng d 020 $a9781484223871 035 $ab14354238-39ule_inst 040 $aBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. Ingegneria Innovazione$beng 082 04$a006.35$223 100 1 $aSarkar, Dipanjan$0785722 245 10$aText analytics with Python :$ba practical real-world approach to gaining actionable insights from your data /$cDipanjan Sarkar 264 1$a[Berkeley, California?] :$bApress,$c2016 300 $axxi, 385 p. ;$c24 cm 500 $aIncludes index 505 0 $aIntroduction -- Natural language basics -- Python refresher -- Processing and understanding text -- Text classification -- Text summarization -- Text similarity and clustering -- Semantic and sentiment analysis. 650 0$aData mining 650 0$aPython (Computer program language) 650 0$aNatural language processing (Computer science) 907 $a.b14354238$b27-11-18$c27-11-18 912 $a991003575949707536 945 $aLE026 006.35 SAR 01.01 2016$g1$i2026000072852$lle026$nProf. Manni / Biblioteca$op$pE43.00$q-$rl$s- $t4$u1$v0$w1$x0$y.i15869568$z27-11-18 996 $aText analytics with Python$91749316 997 $aUNISALENTO 998 $ale026$b11-09-18$cm$da $e $feng$gcau$h0$i0