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The model can fuse topic features to obtain high-quality high-level text features. Experiments show that in binary sentiment classification, the highest classification accuracy of the model can reach more than 90%, which is higher than that of commonly used deep learning models. This paper focuses on the combination of deep neural networks and emerging text processing technologies, and improves and perfects them from two aspects of model architecture and training methods, and designs an efficient deep network sentiment analysis model. A CNN (Convolutional Neural Network) model based on polymorphism is proposed. The model constructs the CNN input matrix by combining the word vector information of the text, the emotion information of the words, and the position information of the words, and adjusts the importance of different feature information in the training process by means of weight control. The multi-objective sample data set is used to verify the effectiveness of the proposed model in the sentiment analysis task of related objects from the classification effect and training performance.<\/jats:p>","DOI":"10.3233\/jifs-179979","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T10:19:53Z","timestamp":1594376393000},"page":"4935-4945","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Negative emotion diffusion and intervention countermeasures of social networks based on deep learning"],"prefix":"10.1177","volume":"39","author":[{"given":"Qiuyun","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Intelligent Engineering, Zhengzhou University of Aeronautics, Henan Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Ke","sequence":"additional","affiliation":[{"name":"Wuhan Technology and Business University College of Humanity&amp;Law, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Abdelmouty","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Informatics, Zagazig University, Alsharkiya, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"issue":"9","key":"e_1_3_2_2_2","first-page":"2048","article-title":"Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis","volume":"39","author":"Sun X.","year":"2017","unstructured":"SunX., PengX., HuM., et al., Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis, Journal of Electronics & Information Technology 39(9) (2017), 2048\u20132055.","journal-title":"Journal of Electronics & Information Technology"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2019.2915543"},{"issue":"5","key":"e_1_3_2_4_2","first-page":"1","article-title":"Automated curation of brand-related social media images with deep learning","volume":"77","author":"Tous R.","year":"2018","unstructured":"TousR., GomezM., PovedaJ., et al., Automated curation of brand-related social media images with deep learning, Multimedia Tools & Applications 77(5) (2018), 1\u201320.","journal-title":"Multimedia Tools & Applications"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0890-9"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-019-0596-4"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-5705-2"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2018.2878852"},{"issue":"5","key":"e_1_3_2_9_2","first-page":"1","article-title":"Multimodal deep learning based on multiple correspondence analysis for disaster management","volume":"22","author":"Pouyanfar S.","year":"2018","unstructured":"PouyanfarS., TaoY., TianH., et al., Multimodal deep learning based on multiple correspondence analysis for disaster management, World Wide Web 22(5) (2018), 1\u201319.","journal-title":"World Wide Web"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2019.2910599"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.54216\/JCIM.020202"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.54216\/JISIoT.010101"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2882635"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2538802"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-44416-8"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2784181"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.54216\/IJNS.010204"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-016-2468-4"},{"issue":"6","key":"e_1_3_2_19_2","first-page":"1012","article-title":"Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images","volume":"12","author":"Li M.-X.","year":"2019","unstructured":"LiM.-X., YuS.-Q., ZhangW., et al., Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images, International Journal of Ophthalmology 12(6) (2019), 1012\u20131020.","journal-title":"International Journal of Ophthalmology"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0887-4"}],"container-title":["Journal of Intelligent &amp; 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