LEADER 01171nlm 2200253Ia 450 001 996413649403316 005 20210430074633.0 100 $a19831116d1687---- uy | 101 0 $aeng 102 $aUK 135 $adrcnu 200 1 $aPerkins$ea new almanack for the year of our Lord God 1687 : being the third after leap-year, and from the worlds creation according to sacred writ, 5636 years : composed and chiefly referred to ... London but (without sensible error) may serve for any other place in Great Britain : adorned with a compendious chronology of things worth remembrance since the creation to this present year : as also the weather, the sun & moons rising & setting, with the high-wayes &c, and many other useful things ...$fmade and set forth by F. Perkins 210 1 $aLondon$cPrinted for the Company of Stationers$d1687 215 $aTesto elettronico (PDF) ([40] p.) 230 $aBase dati testuale 606 0 $aAstrologia$2BNCF 676 $a133.5 700 1$aPERKINS,$bSamuel$f1625-1643$0792756 801 0$bcba$aIT$bcba$gREICAT 912 $a996413649403316 959 $aEB 969 $aER 996 $aPerkins$91772836 997 $aUNISA LEADER 01366nam 2200373 450 001 9910131175203321 005 20240207143711.0 010 $a1-55441-644-2 024 7 $a10.1522/cla.beg.dev 035 $a(CKB)3680000000167485 035 $a(NjHacI)993680000000167485 035 $a(EXLCZ)993680000000167485 100 $a20240207d2005 uy 0 101 0 $afre 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aLe devenir de l'e?tat du Que?bec /$fGe?rard Bergeron 210 1$a[Place of publication not identified] :$cJ.-M. Tremblay,$d2005. 215 $a1 online resource 225 1 $aClassiques des sciences sociales 327 $aPre?sentation, par Ge?rard Bergeron--A? la cafe?te?ria constitutionnelle ... --Un moment d'histoire pluto?t inattendu--Au comptoir de la cafe?te?ria constitutionnelle--Qui parle ? Comment parler ? - Re?fe?rendums ou Constituantes, ou les deux ?--Conclusion. 410 0$aClassiques des sciences sociales. 606 $aNationalism$zQue?bec (Province) 615 0$aNationalism 676 $a320.5409714 700 $aBergeron$b Ge?rard$0406493 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910131175203321 996 $aLe devenir de l'e?tat du Que?bec$93908579 997 $aUNINA LEADER 04253nam 22005775 450 001 9910350225503321 005 20231018185744.0 010 $a981-13-7474-0 024 7 $a10.1007/978-981-13-7474-6 035 $a(CKB)4100000008876798 035 $a(DE-He213)978-981-13-7474-6 035 $a(MiAaPQ)EBC5747484 035 $a(PPN)235669458 035 $a(EXLCZ)994100000008876798 100 $a20190406d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aVisual and Text Sentiment Analysis through Hierarchical Deep Learning Networks /$fby Arindam Chaudhuri 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (XIX, 98 p. 49 illus., 42 illus. in color.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 0 $a981-13-7473-2 327 $aChapter1. Introduction -- Chapter 2. Current State of Art -- Chapter 3. Literature Review -- Chapter 4. Twitter Datasets Used -- Chapter 5. Visual and Text Sentiment Analysis -- Chapter 6. Experimental Setup: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks -- Chapter 7. Twitter Datasets Used -- Chapter 8. Experimental Results -- Chapter 9. Conclusion. 330 $aThis book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book?s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aInformation retrieval 606 $aDatabase management 606 $aData mining 606 $aPattern perception 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aInformation retrieval. 615 0$aDatabase management. 615 0$aData mining. 615 0$aPattern perception. 615 14$aInformation Storage and Retrieval. 615 24$aDatabase Management. 615 24$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 676 $a025.04 700 $aChaudhuri$b Arindam$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763017 906 $aBOOK 912 $a9910350225503321 996 $aVisual and Text Sentiment Analysis through Hierarchical Deep Learning Networks$92495440 997 $aUNINA