{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:23:26Z","timestamp":1761805406350,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,3,9]],"date-time":"2019-03-09T00:00:00Z","timestamp":1552089600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,3,9]],"date-time":"2019-03-09T00:00:00Z","timestamp":1552089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s11063-019-10017-9","type":"journal-article","created":{"date-parts":[[2019,3,9]],"date-time":"2019-03-09T12:27:23Z","timestamp":1552134443000},"page":"2089-2103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Multi-layer Attention Based CNN for Target-Dependent Sentiment Classification"],"prefix":"10.1007","volume":"51","author":[{"given":"Suqi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xinyun","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yanwei","family":"Pang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-956X","authenticated-orcid":false,"given":"Jungong","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,9]]},"reference":[{"key":"10017_CR1","doi-asserted-by":"crossref","unstructured":"Zhao S, Ding G, Gao Y, Han J (2017) Approximating discrete probability distribution of image emotions by multi-modal features fusion. In: Proceeding of the twenty-sixth international joint conference on artificial intelligence, pp 4669\u20134675","DOI":"10.24963\/ijcai.2017\/651"},{"issue":"3","key":"10017_CR2","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1109\/TMM.2016.2617741","volume":"19","author":"S Zhao","year":"2016","unstructured":"Zhao S, Yao H, Gao Y, Ji R, Ding G (2016) Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans Multimed 19(3):632\u2013645","journal-title":"IEEE Trans Multimed"},{"issue":"4","key":"10017_CR3","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TAFFC.2016.2628787","volume":"9","author":"S Zhao","year":"2018","unstructured":"Zhao S, Yao H, Gao Y, Ding G, Chua T (2018) Predicting personalized image emotion perceptions in social networks. IEEE Trans Affect Comput 9(4):526\u2013540","journal-title":"IEEE Trans Affect Comput"},{"issue":"11","key":"10017_CR4","doi-asserted-by":"publisher","first-page":"3218","DOI":"10.1109\/TCYB.2017.2762344","volume":"48","author":"S Zhao","year":"2018","unstructured":"Zhao S, Gao Y, Ding G, Chua T (2018) Real-time multimedia social event detection in microblog. IEEE Trans Cybern 48(11):3218\u20133231","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"10017_CR5","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","volume":"5","author":"W Medhat","year":"2014","unstructured":"Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093\u20131113","journal-title":"Ain Shams Eng J"},{"issue":"1","key":"10017_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00416ED1V01Y201204HLT016","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1\u2013167","journal-title":"Synth Lect Hum Lang Technol"},{"key":"10017_CR7","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the twenty-eighth international conference on machine learning, pp 513\u2013520"},{"issue":"2","key":"10017_CR8","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.eswa.2012.07.059","volume":"40","author":"R Moraes","year":"2013","unstructured":"Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621\u2013633","journal-title":"Expert Syst Appl"},{"key":"10017_CR9","unstructured":"Santos CND, Gattit M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the twenty-fifth international conference on computational linguistics: technical papers, pp 69\u201378"},{"key":"10017_CR10","doi-asserted-by":"crossref","unstructured":"Yu J, Jiang J (2016) Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 236\u2013246","DOI":"10.18653\/v1\/D16-1023"},{"key":"10017_CR11","doi-asserted-by":"crossref","unstructured":"Dong L, Wei F, Tan C, Tang D, Zhou M, Xu K (2014) Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the fifty-second annual meeting of the association for computational linguistics (volume 2: short papers), vol 2, pp 49\u201354","DOI":"10.3115\/v1\/P14-2009"},{"key":"10017_CR12","unstructured":"Vo DT, Zhang Y (2015) Target-dependent twitter sentiment classification with rich automatic features. In: Proceedings of the twenty-forth international joint conference on artificial intelligence, pp 1347\u20131353"},{"key":"10017_CR13","unstructured":"Tang D, Qin B, Feng X, Liu T (2016) Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the international conference on computational linguistics: technical papers, pp 3298\u20133307"},{"key":"10017_CR14","doi-asserted-by":"crossref","unstructured":"Zhang M, Zhang Y, Vo DT (2016) Gated neural networks for targeted sentiment analysis. In: Proceedings of AAAI conference on artificial intelligence, pp 3087\u20133093","DOI":"10.1609\/aaai.v30i1.10380"},{"key":"10017_CR15","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhao L et al (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606\u2013615","DOI":"10.18653\/v1\/D16-1058"},{"key":"10017_CR16","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 214\u2013224","DOI":"10.18653\/v1\/D16-1021"},{"key":"10017_CR17","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. In: Proceeding of the twenty-sixth international joint conference on artificial intelligence, pp 4068\u20134074","DOI":"10.24963\/ijcai.2017\/568"},{"key":"10017_CR18","unstructured":"Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017) Convolutional sequence to sequence learning. arXiv preprint arXiv:170503122"},{"key":"10017_CR19","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"10017_CR20","doi-asserted-by":"crossref","unstructured":"Ding X, Liu B (2007) The utility of linguistic rules in opinion mining. In: Proceedings of the thirtieth annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 811\u2013812","DOI":"10.1145\/1277741.1277921"},{"key":"10017_CR21","doi-asserted-by":"crossref","unstructured":"Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 168\u2013177","DOI":"10.1145\/1014052.1014073"},{"key":"10017_CR22","doi-asserted-by":"crossref","unstructured":"Kiritchenko S, Zhu X, Cherry C, Mohammad S (2014) NRC-canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the eighth international workshop on semantic evaluation (SemEval 2014), pp 437\u2013442","DOI":"10.3115\/v1\/S14-2076"},{"key":"10017_CR23","doi-asserted-by":"crossref","unstructured":"Nasukawa T, Yi J (2003) Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the second international conference on knowledge capture. ACM, pp 70\u201377","DOI":"10.1145\/945645.945658"},{"key":"10017_CR24","unstructured":"Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: Proceedings of the forty-ninth annual meeting of the Association for Computational Linguistics: human language technologies-volume 1. Association for Computational Linguistics, pp 151\u2013160"},{"key":"10017_CR25","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:14090473"},{"key":"10017_CR26","doi-asserted-by":"crossref","unstructured":"Wang W, Yang N, Wei F, Chang B, Zhou M (2017) Gated self-matching networks for reading comprehension and question answering. In: Proceedings of the fifty-fifth annual meeting of the association for computational linguistics (volume 1: long papers), vol 1, pp 189\u2013198","DOI":"10.18653\/v1\/P17-1018"},{"key":"10017_CR27","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of international workshop on semantic evaluation, pp 27\u201335","DOI":"10.3115\/v1\/S14-2004"},{"key":"10017_CR28","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"issue":"3","key":"10017_CR29","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/TIP.2017.2651375","volume":"26","author":"N Wang","year":"2017","unstructured":"Wang N, Gao X, Sun L, Li J (2017) Bayesian face sketch synthesis. IEEE Trans Image Process 26(3):1264\u20131274","journal-title":"IEEE Trans Image Process"},{"key":"10017_CR30","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.patcog.2017.11.008","volume":"76","author":"N Wang","year":"2018","unstructured":"Wang N, Gao X, Li J (2018) Random sampling for fast face sketch synthesis. Pattern Recogn 76:215\u2013227","journal-title":"Pattern Recogn"},{"issue":"9","key":"10017_CR31","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1109\/TCSVT.2017.2709465","volume":"28","author":"N Wang","year":"2018","unstructured":"Wang N, Gao X, Sun L, Li J (2018) Anchored neighbourhood index for face sketch synthesis. IEEE Trans Circuits Syst Video Technol 28(9):2154\u20132163","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"10017_CR32","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/TIP.2017.2777183","volume":"27","author":"X Lan","year":"2018","unstructured":"Lan X, Zhang S, Yuan P, Chellappa R (2018) Learning common and feature-specific patterns: a novel multiple sparse representation based tracker. IEEE Trans Image Process 27(4):2022\u20132037","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"10017_CR33","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1109\/TCE.2012.6227420","volume":"58","author":"J Han","year":"2012","unstructured":"Han J, Pauwels P, de Zeeuw P, de With P (2012) Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment. IEEE Trans Consum Electron 58(2):255\u2013263","journal-title":"IEEE Trans Consum Electron"},{"issue":"12","key":"10017_CR34","doi-asserted-by":"publisher","first-page":"5826","DOI":"10.1109\/TIP.2015.2481325","volume":"24","author":"X Lan","year":"2015","unstructured":"Lan X, Ma J, Yuan P, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826\u20135841","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"10017_CR35","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/TITS.2017.2774778","volume":"19","author":"G Ding","year":"2018","unstructured":"Ding G, Chen W, Zhao S, Han J, Liu Q (2018) Real-time scalable visual tracking via quadrangle kernelized correlation filters. IEEE Trans Intell Transp Syst 19(1):140\u2013150","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"12","key":"10017_CR36","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1109\/TMM.2018.2838320","volume":"20","author":"C Yan","year":"2018","unstructured":"Yan C, Xie H, Chen J, Zha Z, Hao X, Zhang Y, Dai Q (2018) A fast Uyghur text detection for complex background images. IEEE Trans Multimed 20(12):3389\u20133398","journal-title":"IEEE Trans Multimed"},{"issue":"5","key":"10017_CR37","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/TPAMI.2016.2567393","volume":"39","author":"D Zhang","year":"2017","unstructured":"Zhang D, Meng D, Han J (2017) Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans Pattern Anal Mach Intell 39(5):865\u2013878","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"10017_CR38","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","volume":"54","author":"G Cheng","year":"2016","unstructured":"Cheng G, Zhou P, Han J (2016) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405\u20137415","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"10017_CR39","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TITS.2017.2749965","volume":"19","author":"C Yan","year":"2018","unstructured":"Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284\u2013295","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"10017_CR40","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TIP.2018.2882155","volume":"28","author":"G Wu","year":"2019","unstructured":"Wu G, Han J, Guo Y, Liu L, Ding G, Ni Q, Shao L (2019) Unsupervised deep video hashing via balanced code for large-scale video retrieval. IEEE Trans Image Process 28(4):1993\u20132007","journal-title":"IEEE Trans Image Process"},{"key":"10017_CR41","doi-asserted-by":"publisher","DOI":"10.1109\/tie.2018.2873547","author":"G Wu","year":"2019","unstructured":"Wu G, Han J, Lin Z, Ding G, Zhang B, Ni Q (2019) Joint image-text hashing for fast large-scale cross-media retrieval using self-supervised deep learning. IEEE Trans Ind Electron. https:\/\/doi.org\/10.1109\/tie.2018.2873547 (in press)","journal-title":"IEEE Trans Ind Electron"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-019-10017-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-019-10017-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-019-10017-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T16:03:28Z","timestamp":1663085008000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-019-10017-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,9]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["10017"],"URL":"https:\/\/doi.org\/10.1007\/s11063-019-10017-9","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2019,3,9]]},"assertion":[{"value":"9 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}