LEADER 01735nam0 2200409 450 001 000036090 005 20180621110901.0 100 $a20140305d1950----km-y0itaa50------ba 101 0 $aita 102 $aIT 200 1 $aAnticrittogamici insetticidi e diserbanti$fUgo Pratolongo 205 $a3. ed. riveduta ed ampliata 210 $aRoma$cRamo editoriale degli agricoltori$d1950 215 $a322 p.$d25 cm 225 2 $aTrattati di agricoltura$v2 300 $aCon un indice delle cagioni nemiche e dei loro rimedi, ordinato secondo le colture attaccate 316 $aSulla pagina precedente il frontespizio dell'esemplare FVig/T41391: Gioacchino Viggiani 30-6-50 410 0$12001$aTrattati di agricoltura$v2 606 1 $aAnticrittogamici 606 2 $aInsetticidi 606 2 $aDiserbanti 676 $a668.651$v(22. ed.)$9Insetticidi, rodenticidi, vermicidi 676 $a632.95$v(22. ed.)$9Controllo delle infestazioni e malattie delle piante. Pesticidi 676 $a668.654$v(22. ed.)$9Erbicidi 700 1$aPratolongo,$bUgo$f<1887-1968>$0757860 801 0$aIT$bUniversità della Basilicata - B.I.A.$gREICAT$2unimarc 912 $a000036090 996 $aAnticrittogamici insetticidi e diserbanti$91529657 997 $aUNIBAS CAT $aSTD094$b01$c20140305$lBAS01$h0830 CAT $aTTM$b30$c20140321$lBAS01$h0942 CAT $aATR$b20$c20180529$lBAS01$h1105 CAT $aATR$b20$c20180529$lBAS01$h1110 CAT $aBATCH-UPD$b20$c20180529$lBAS01$h1112 CAT $aATR$b20$c20180621$lBAS01$h1109 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA2$APolo Tecnico-Scientifico$2FVIG$BFondo Viggiani$3FVig/41391$641391$5T41391$7Collocato presso la Scuola di Agraria$820140305$f35$FStanza riservata LEADER 03079nam 2200793z- 450 001 9910404092303321 005 20210212 010 $a3-03928-573-4 035 $a(CKB)4100000011302215 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/59238 035 $a(oapen)doab59238 035 $a(oapen)59238 035 $a(EXLCZ)994100000011302215 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSentiment Analysis for Social Media 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (152 p.) 311 08$a3-03928-572-6 330 $aSentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection. 606 $aHistory of engineering and technology$2bicssc 610 $aaffect computing 610 $abig data-driven marketing 610 $acollaborative schemes of sentiment analysis and sentiment systems 610 $aconvolutional neural network 610 $acyber-aggression 610 $adeep learning 610 $aemotion analysis 610 $aemotion classification 610 $agender classification 610 $ahealth insurance 610 $ahybrid vectorization 610 $alexicon construction 610 $amachine learning 610 $amedical web forum 610 $aonline review 610 $aopinion mining 610 $aprovider networks 610 $apsychographic segmentation 610 $aracism 610 $arandom forest 610 $arecommender system 610 $areview data mining 610 $asemantic networks 610 $asentiment analysis 610 $asentiment classification 610 $asentiment lexicon 610 $asentiment word analysis 610 $asentiment-aware word embedding 610 $asocial media 610 $asocial networks 610 $atext feature representation 610 $atext mining 610 $aTwitter 610 $auser preference prediction 610 $aviolence against women 610 $aviolence based on sexual orientation 610 $aword association 615 7$aHistory of engineering and technology 700 $aMoreno$b Anto?nio$4auth$0419397 702 $aIglesias$b Carlos A$4auth 906 $aBOOK 912 $a9910404092303321 996 $aSentiment Analysis for Social Media$93020240 997 $aUNINA