LEADER 01845nam 2200577 450 001 9910787836103321 005 20230126212302.0 010 $a0-8156-5263-1 035 $a(CKB)2670000000560430 035 $a(EBL)4649092 035 $a(SSID)ssj0001260984 035 $a(PQKBManifestationID)11694210 035 $a(PQKBTitleCode)TC0001260984 035 $a(PQKBWorkID)11321202 035 $a(PQKB)10476374 035 $a(MiAaPQ)EBC4649092 035 $a(OCoLC)881319599 035 $a(MdBmJHUP)muse33184 035 $a(EXLCZ)992670000000560430 100 $a20160906h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe Moroccan women's rights movement /$fAmy Young Evrard 205 $aFirst edition. 210 1$aSyracuse, New York :$cSyracuse University Press,$d2014. 210 4$dİ2014 215 $a1 online resource (316 p.) 225 1 $aGender and Globalization 300 $aDescription based upon print version of record. 311 $a0-8156-3350-5 320 $aIncludes bibliographical references and index. 327 $aConvincing women -- Obstacles and opportunities -- Vernacularizing frames: "equality" and "women's human rights" -- Framing Mudawwana reform -- The harmonious family. 410 0$aGender and globalization. 606 $aFeminism$zMorocco 606 $aWomen's rights$zMorocco 606 $aWomen$zMorocco$xSocial conditions 615 0$aFeminism 615 0$aWomen's rights 615 0$aWomen$xSocial conditions. 676 $a305.40964 700 $aEvrard$b Amy Young$01515480 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910787836103321 996 $aThe Moroccan women's rights movement$93751246 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