LEADER 01692nas 2200433 450 001 9910717032403321 005 20211229120218.0 035 $a(CKB)5470000002527884 035 $a(OCoLC)1290318706 035 $a(EXLCZ)995470000002527884 100 $a20211229a20189999 ua 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHigh-octane gasoline from lignocellulosic biomass via syngas and methanol/dimethyl ether intermediates $e... state of technology and future research 210 1$aGolden, CO :$cNational Renewable Energy Laboratory,$d2018- 215 $a1 online resource (volumes) $cillustrations 225 1 $aNREL/TP 517 $aHigh-octane gasoline from lignocellulosic biomass via syngas and methanol/dimethyl ether intermediates 606 $aGasoline$zUnited States$xAnti-knock and anti-knock mixtures$vPeriodicals 606 $aBiomass chemicals$zUnited States$vPeriodicals 606 $aLignocellulose$xBiotechnology$zUnited States$vPeriodicals 606 $aSynthesis gas$vPeriodicals 606 $aIntermediates (Chemistry)$vPeriodicals 608 $aTechnical reports.$2lcgft 615 0$aGasoline$xAnti-knock and anti-knock mixtures 615 0$aBiomass chemicals 615 0$aLignocellulose$xBiotechnology 615 0$aSynthesis gas 615 0$aIntermediates (Chemistry) 712 02$aNational Renewable Energy Laboratory (U.S.), 801 0$bGPO 801 1$bGPO 906 $aJOURNAL 912 $a9910717032403321 996 $aHigh-octane gasoline from lignocellulosic biomass via syngas and methanol$93303560 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