LEADER 02770nam 22006013u 450 001 9910781820603321 005 20230617035035.0 010 $a1-281-22836-2 010 $a9786611228361 010 $a1-59385-953-8 035 $a(CKB)1000000000484623 035 $a(EBL)330505 035 $a(OCoLC)437198464 035 $a(SSID)ssj0000114227 035 $a(PQKBManifestationID)11984674 035 $a(PQKBTitleCode)TC0000114227 035 $a(PQKBWorkID)10125038 035 $a(PQKB)10426779 035 $a(MiAaPQ)EBC330505 035 $a(EXLCZ)991000000000484623 100 $a20130418d2005|||| u|| | 101 0 $aeng 181 $ctxt 182 $cc 183 $acr 200 10$aBoys of Few Words$b[electronic resource] $eRaising Our Sons to Communicate and Connect 210 $aNew York $cGuilford Press$d2005 215 $a1 online resource (352 p.) 300 $aDescription based upon print version of record. 311 $a1-59385-218-5 327 $aPreliminaries; SEVEN Navigating the Challenges of Learning and Attention Problems; Contents; EIGHT Ten Commitments to Boys Communication; Prologue; ONE Is Your Son a Boy of Few Words; TWO Why Words Matter; THREE Why Doesn't He Talk to Me; FOUR Without Words for Emotion; Helpful Resources; FIVE Encouraging Shy and Withdrawn Boys; Bibliography; SIX Reducing the Resistance of Angry; Index; About the Author; NINE Leading Boys across the Divide; TEN Working with Schools; ELEVEN When Professional Help Makes Sense; Epilogue The Men They Will Become 330 $aWhen parents feel separated from their sons by a curtain of silence or a wall of resistance, they're right to be concerned. Boys of few words--the ones who limit their expression to a timid shrug or an indifferent grunt--need our help. Whether the problem is rooted in 'nature' or 'nurture,' boys who grow up unable to talk about their thoughts and feelings find it hard to connect with others at school, home, and eventually in business and personal relationships. Psychologist Adam Cox helps parents understand all the factors that may be limiting their son's ability or willingness to communicate- 606 $aBoys 606 $aChild rearing 606 $aCommunication in families 606 $aBoys 606 $aChild rearing 606 $aCommunication in families 615 4$aBoys. 615 4$aChild rearing. 615 4$aCommunication in families. 615 0$aBoys 615 0$aChild rearing 615 0$aCommunication in families 676 $a649.132 700 $aCox$b Adam J$01554396 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910781820603321 996 $aBoys of Few Words$93815614 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