{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:15:53Z","timestamp":1768284953431,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Found","doi-asserted-by":"crossref","award":["2021M692135"],"award-info":[{"award-number":["2021M692135"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Philosophy and Social Science Planning Project","award":["2021BTQ003"],"award-info":[{"award-number":["2021BTQ003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Microblog sentiment analysis has important applications in many fields, such as social media analysis and online product reviews. However, the traditional methods may be challenging to compute the long dependencies between them and easy to lose some semantic information due to low standardization of text and emojis in microblogs. In this paper, we propose a novel deep memory network with structural self-attention, storing long-term contextual information and extracting richer text and emojis information from microblogs, which aims to improve the performance of sentiment analysis. Specifically, the model first utilizes a bidirectional long short-term memory network to extract the semantic information in the microblogs, and considers the extraction results as the memory component of the deep memory network, storing the long dependencies and free of syntactic parser, sentiment lexicon and feature engineering. Then, we consider multi-step structural self-attention operations as the generalization and output components. Furthermore, this study also employs a penalty mechanism to the loss function to promote the diversity across different hops of attention in the model. This study conducted extensive comprehensive experiments with eight baseline methods on real datasets. Results show that our model outperforms those state-of-the-art models, which validates the effectiveness of the proposed model.<\/jats:p>","DOI":"10.1007\/s40747-022-00904-5","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T11:03:41Z","timestamp":1668769421000},"page":"3071-3083","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Microblog sentiment analysis based on deep memory network with structural attention"],"prefix":"10.1007","volume":"9","author":[{"given":"Lixin","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4888-4023","authenticated-orcid":false,"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Laijun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Pingle","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"904_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2020.106633","volume":"116","author":"Z Bastick","year":"2021","unstructured":"Bastick Z (2021) Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation. Comput Hum Behav 116:106633. https:\/\/doi.org\/10.1016\/j.chb.2020.106633","journal-title":"Comput Hum Behav"},{"issue":"1","key":"904_CR2","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/TCSS.2018.2885127","volume":"6","author":"X Liu","year":"2019","unstructured":"Liu X, He D, Liu C (2019) Information diffusion nonlinear dynamics modeling and evolution analysis in online social network based on emergency events. IEEE Trans Comput Soc Syst 6(1):8\u201319. https:\/\/doi.org\/10.1109\/TCSS.2018.2885127","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"904_CR3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.jbankfin.2018.09.016","volume":"96","author":"S Behrendt","year":"2018","unstructured":"Behrendt S, Schmidt A (2018) The Twitter myth revisited: intraday investor sentiment, Twitter activity and individual-level stock return volatility. J Bank Financ 96:355\u2013367. https:\/\/doi.org\/10.1016\/j.jbankfin.2018.09.016","journal-title":"J Bank Financ"},{"issue":"1","key":"904_CR4","doi-asserted-by":"publisher","first-page":"129","DOI":"10.15837\/ijccc.2018.1.3176","volume":"13","author":"H Wei-dong","year":"2018","unstructured":"Wei-dong H, Qian W, Jie C (2018) Tracing public opinion propagation and emotional evolution based on public emergencies in social networks. Int J Comput Commun 13(1):129\u2013142 https:\/\/doi.org\/10.15837\/ijccc.2018.1.3176","journal-title":"Int J Comput Commun"},{"issue":"3","key":"904_CR5","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1108\/IDD-10-2019-0074","volume":"48","author":"L Zhang","year":"2020","unstructured":"Zhang L, Wei J, Boncella RJ (2020) Emotional communication analysis of emergency microblog based on the evolution life cycle of public opinion. Inform Discovery Delivery 48(3):151\u2013163 https:\/\/doi.org\/10.1108\/IDD-10-2019-0074","journal-title":"Inform Discovery Delivery"},{"issue":"1","key":"904_CR6","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"Z. H.","year":"2018","unstructured":"Z. H. (2018) A brief introduction to weakly supervised learning. Natl Sci Rev 5(1):44\u201353. https:\/\/doi.org\/10.1093\/nsr\/nwx106","journal-title":"Natl Sci Rev"},{"issue":"4","key":"904_CR7","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/TMM.2017.2757769","volume":"20","author":"F Chen","year":"2017","unstructured":"Chen F, Ji R, Su J et al (2017) Predicting microblog sentiments via weakly supervised multimodal deep learning. IEEE T Multimedia 20(4):997\u20131007. https:\/\/doi.org\/10.1109\/TMM.2017.2757769","journal-title":"IEEE T Multimedia"},{"key":"904_CR8","doi-asserted-by":"publisher","first-page":"23319","DOI":"10.1109\/ACCESS.2019.2899260","volume":"7","author":"A Kumar","year":"2019","unstructured":"Kumar A, Sangwan SR, Arora A, Nayyar A, Abdel-Basset M (2019) Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE access 7:23319\u201323328. https:\/\/doi.org\/10.1109\/ACCESS.2019.2899260","journal-title":"IEEE access"},{"key":"904_CR9","doi-asserted-by":"publisher","unstructured":"Sun B, Tian F, Liang L (2018) Tibetan micro-blog sentiment analysis based on mixed deep learning. In: 2018 international conference on audio, language and image processing (ICALIP), pp 109\u2013112 https:\/\/doi.org\/10.1109\/ICALIP.2018.8455328","DOI":"10.1109\/ICALIP.2018.8455328"},{"issue":"5","key":"904_CR10","doi-asserted-by":"publisher","first-page":"945","DOI":"10.7544\/issn1000-1239.2018.20170049","volume":"55","author":"C Ke","year":"2018","unstructured":"Ke C, Bin L, Wende K, Bo X, Guochao Z (2018) Chinese micro-blog sentiment analysis based on multi-channels convolutional neural networks. J Comput Res Dev 55(5):945. https:\/\/doi.org\/10.7544\/issn1000-1239.2018.20170049","journal-title":"J Comput Res Dev"},{"key":"904_CR11","unstructured":"Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473."},{"key":"904_CR12","doi-asserted-by":"publisher","unstructured":"Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep Learning--based Text Classification: A Comprehensive Review. ACM Computing Surveys (CSUR), 54(3):1\u201340. https:\/\/doi.org\/10.48550\/arXiv.2004.03705","DOI":"10.48550\/arXiv.2004.03705"},{"issue":"5","key":"904_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3465055","volume":"12","author":"S Chaudhari","year":"2021","unstructured":"Chaudhari S, Mithal V, Polatkan G, Ramanath R (2021) An attentive survey of attention models. ACM Trans Intell Syst Technol (TIST) 12(5):1\u201332","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"904_CR14","doi-asserted-by":"publisher","first-page":"17644","DOI":"10.1109\/ACCESS.2019.2895897","volume":"7","author":"L Li","year":"2019","unstructured":"Li L, Wu Y, Zhang Y, Zhao T (2019) Time + user dual attention based sentiment prediction for multiple social network texts with time series. IEEE Access 7:17644\u201317653. https:\/\/doi.org\/10.1109\/ACCESS.2019.2895897","journal-title":"IEEE Access"},{"key":"904_CR15","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.future.2020.05.022","volume":"112","author":"C Gan","year":"2020","unstructured":"Gan C, Wang L, Zhang Z (2020) Multi-entity sentiment analysis using self-attention based hierarchical dilated convolutional neural network. Future Gener Comp Sy 112:116\u2013125. https:\/\/doi.org\/10.1016\/j.future.2020.05.022","journal-title":"Future Gener Comp Sy"},{"key":"904_CR16","doi-asserted-by":"publisher","unstructured":"Ding J, Sun HL, Wang X, Liu XD. (2018). Entity-level sentiment analysis of issue comments. In: Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, ACM. pp 7\u201313 https:\/\/doi.org\/10.1145\/3194932.3194935","DOI":"10.1145\/3194932.3194935"},{"issue":"2","key":"904_CR17","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. https:\/\/doi.org\/10.1016\/j.eswa.2012.07.059","journal-title":"Expert Syst Appl"},{"key":"904_CR18","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.ins.2022.03.038","volume":"596","author":"L Zhou","year":"2022","unstructured":"Zhou L, Zhang Z, Zhao L (2022) Attention-based bilstm models for personality recognition from user-generated content. Inform Sci 596:460\u2013471. https:\/\/doi.org\/10.1016\/j.ins.2022.03.038","journal-title":"Inform Sci"},{"key":"904_CR19","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","volume":"72","author":"T Chen","year":"2017","unstructured":"Chen T, Xu R, He Y (2017) Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst Appl 72:221\u2013230. https:\/\/doi.org\/10.1016\/j.eswa.2016.10.065","journal-title":"Expert Syst Appl"},{"key":"904_CR20","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2018.04.045","volume":"308","author":"G Rao","year":"2018","unstructured":"Rao G, Huang W, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49\u201357. https:\/\/doi.org\/10.1016\/j.neucom.2018.04.045","journal-title":"Neurocomputing"},{"issue":"3","key":"904_CR21","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1007\/s11063-021-10423-y","volume":"53","author":"J Chen","year":"2021","unstructured":"Chen J, Yu J, Zhao S, Zhang Y (2021) User\u2019s review habits enhanced hierarchical neural network for document-level sentiment classification. Neural Process Lett 53(3):2095\u20132111. https:\/\/doi.org\/10.1007\/s11063-021-10423-y","journal-title":"Neural Process Lett"},{"key":"904_CR22","doi-asserted-by":"publisher","unstructured":"Sukhbaatar, S., Weston, J., & Fergus, R. (2015). End-to-end memory networks. Advances in neural information processing systems, pp 28. https:\/\/doi.org\/10.48550\/arXiv.1503.08895","DOI":"10.48550\/arXiv.1503.08895"},{"key":"904_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3019893","author":"A Galassi","year":"2020","unstructured":"Galassi A, Lippi M, Torroni P (2020) Attention in natural language processing. IEEE T Neur Net Lear. https:\/\/doi.org\/10.1109\/TNNLS.2020.3019893","journal-title":"IEEE T Neur Net Lear"},{"key":"904_CR24","doi-asserted-by":"publisher","unstructured":"Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell M, 13(3):55\u201375. https:\/\/doi.org\/10.48550\/arXiv.1708.02709","DOI":"10.48550\/arXiv.1708.02709"},{"key":"904_CR25","doi-asserted-by":"publisher","unstructured":"Chen K, Chen JK, Chuang J, V\u00e1zquez M, Savarese S (2021) Topological Planning with Transformers for Vision-and-Language Navigation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11276\u201311286. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01112","DOI":"10.1109\/CVPR46437.2021.01112"},{"key":"904_CR26","doi-asserted-by":"publisher","unstructured":"Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. pp 1412\u20131421. https:\/\/doi.org\/10.18653\/v1\/D15-1166","DOI":"10.18653\/v1\/D15-1166"},{"key":"904_CR27","doi-asserted-by":"publisher","unstructured":"Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations, ICLR 2015. https:\/\/doi.org\/10.48550\/arXiv.1409.0473","DOI":"10.48550\/arXiv.1409.0473"},{"key":"904_CR28","first-page":"6000","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Noam S, Niki P, Jakob U, Llion J, Aidan NG, \u0141ukasz K, Illia P (2017) Attention is all you need. Adv Neural inform Process Syst 30:6000\u20136010","journal-title":"Adv Neural inform Process Syst"},{"key":"904_CR29","doi-asserted-by":"publisher","unstructured":"Mao R, Lin C, Guerin F (2019) End-to-end sequential metaphor identification inspired by linguistic theories. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 3888\u20133898. https:\/\/doi.org\/10.18653\/v1\/P19-1378","DOI":"10.18653\/v1\/P19-1378"},{"key":"904_CR30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10974","author":"W Wang","year":"2017","unstructured":"Wang W, Pan SJ, Dahlmeier D, Xiao X (2017) Coupled multi-layer attentions for co-extraction of aspect and opinion terms. Proc AAAI Conf Artificial Intell. https:\/\/doi.org\/10.1609\/aaai.v31i1.10974","journal-title":"Proc AAAI Conf Artificial Intell"},{"key":"904_CR31","unstructured":"Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165."},{"issue":"3","key":"904_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439726","volume":"54","author":"S Minaee","year":"2021","unstructured":"Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J (2021) Deep learning-based text classification: a comprehensive review. ACM Comput Surv 54(3):1\u201340. https:\/\/doi.org\/10.1145\/3439726","journal-title":"ACM Comput Surv"},{"key":"904_CR33","doi-asserted-by":"crossref","unstructured":"Eisner B, Rockt\u00e4schel T, Augenstein I, Bo\u0161njak M, Riedel S (2016) Emoji2vec: Learning emoji representations from their description. arXiv preprint arXiv:1609.08359.","DOI":"10.18653\/v1\/W16-6208"},{"key":"904_CR34","doi-asserted-by":"publisher","unstructured":"Hitesh MSR, Vaibhav V, Kalki YA, Kamtam SH, Kumari S (2019) Real-time sentiment analysis of 2019 election tweets using word2vec and random forest model. In 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), pp 146\u2013151. https:\/\/doi.org\/10.1109\/ICCT46177.2019.8969049","DOI":"10.1109\/ICCT46177.2019.8969049"},{"key":"904_CR35","doi-asserted-by":"publisher","unstructured":"Kurnia R, Tangkuman Y, Girsang A (2020) Classification of User Comment Using Word2vec and SVM Classifier. Int. J. Adv. Trends Comput. Sci. Eng, 9:643\u2013648. https:\/\/doi.org\/10.30534\/ijatcse\/2020\/90912020","DOI":"10.30534\/ijatcse\/2020\/90912020"},{"issue":"7","key":"904_CR36","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural comput 31(7):1235\u20131270. https:\/\/doi.org\/10.1162\/neco_a_01199","journal-title":"Neural comput"},{"key":"904_CR37","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/ACCESS.2019.2909919","volume":"51","author":"G Xu","year":"2019","unstructured":"Xu G, Meng Y, Qiu X, Yu Z, Wu X (2019) Sentiment analysis of comment texts based on BiLSTM. IEEE Access 51:2\u201351532. https:\/\/doi.org\/10.1109\/ACCESS.2019.2909919","journal-title":"IEEE Access"},{"key":"904_CR38","doi-asserted-by":"crossref","unstructured":"Qiu Y, Li H, Li S, Jiang Y, Hu R, Yang L (2018) Revisiting correlations between intrinsic and extrinsic evaluations of word embeddings. Proceedings of the Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, Changsha, pp 209\u2013221.","DOI":"10.1007\/978-3-030-01716-3_18"},{"key":"904_CR39","unstructured":"Devlin J, Chang MW, Lee K, Toutanova . (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"904_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115279","volume":"184","author":"M Fern\u00e1ndez-Gavilanes","year":"2021","unstructured":"Fern\u00e1ndez-Gavilanes M, Costa-Montenegro E, Garc\u00eda-M\u00e9ndez S, Gonz\u00e1lez-Casta\u00f1o FJ, Juncal-Mart\u00ednez J (2021) Evaluation of online emoji description resources for sentiment analysis purposes. Expert Syst Appl 184:115279. https:\/\/doi.org\/10.1016\/j.eswa.2021.115279","journal-title":"Expert Syst Appl"},{"key":"904_CR41","doi-asserted-by":"publisher","unstructured":"Mostafavi M, Porter MD (2021) How emoji and word embedding helps to unveil emotional transitions during online messaging. In 2021 IEEE International Systems Conference. pp 1\u20138. https:\/\/doi.org\/10.1109\/SysCon48628.2021.9447137","DOI":"10.1109\/SysCon48628.2021.9447137"},{"key":"904_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109220","author":"L Chen","year":"2022","unstructured":"Chen L, Tingting C, Lixin Z (2022) Learning to rank complex network node based on the self-supervised graph convolution model. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2022.109220","journal-title":"Knowl-Based Syst"},{"key":"904_CR43","doi-asserted-by":"publisher","unstructured":"Rao A, Ahuja A, Kansara S, Patel V (2021) Sentiment analysis on user-generated video, audio and text. In: 2021 International Conference on Computing, Communication, and Intelligent Systems, pp 24\u201328. https:\/\/doi.org\/10.1109\/ICCCIS51004.2021.9397147","DOI":"10.1109\/ICCCIS51004.2021.9397147"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00904-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-022-00904-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00904-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T17:04:31Z","timestamp":1686330271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-022-00904-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,18]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["904"],"URL":"https:\/\/doi.org\/10.1007\/s40747-022-00904-5","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,18]]},"assertion":[{"value":"24 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}