{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:17:31Z","timestamp":1743067051213,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031379390"},{"type":"electronic","value":"9783031379406"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-37940-6_28","type":"book-chapter","created":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T20:23:00Z","timestamp":1690057380000},"page":"340-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8908-5027","authenticated-orcid":false,"given":"Brajesh Kumar","family":"Shrivash","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9288-6819","authenticated-orcid":false,"given":"Dinesh Kumar","family":"Verma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5384-6606","authenticated-orcid":false,"given":"Prateek","family":"Pandey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,23]]},"reference":[{"issue":"12","key":"28_CR1","doi-asserted-by":"publisher","first-page":"13911","DOI":"10.1007\/s11227-021-03838-w","volume":"77","author":"I Priyadarshini","year":"2021","unstructured":"Priyadarshini, I., Cotton, C.: A novel LSTM\u2013CNN\u2013grid search-based deep neural network for sentiment analysis. J. Supercomput. 77(12), 13911\u201313932 (2021). https:\/\/doi.org\/10.1007\/s11227-021-03838-w","journal-title":"J. Supercomput."},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Huang, F., Li, X., Yuan, C., Zhang, S., Zhang, J., Qiao, S.: Attention-emotion-enhanced convolutional LSTM for sentiment analysis. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3056664"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Priyadarshini, I., Mohanty, P., Kumar, R., Sharma, R., Puri, V., Singh, P.K.: A study on the sentiments and psychology of Twitter users during the COVID-19 lockdown period. Multimed. Tools Appl. 1\u201323 (2021)","DOI":"10.1007\/s11042-021-11004-w"},{"key":"28_CR4","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-981-16-2877-1_46","volume-title":"Smart Systems: Innovations in Computing","author":"BK Shrivash","year":"2022","unstructured":"Shrivash, B.K., Verma, D.K., Pandey, P.: An analysis on machine learning approaches for sentiment analysis. In: Somani, A.K., Mundra, A., Doss, R., Bhattacharya, S. (eds.) Smart Systems: Innovations in Computing. SIST, vol. 235, pp. 499\u2013513. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-2877-1_46"},{"key":"28_CR5","doi-asserted-by":"publisher","first-page":"15561","DOI":"10.1109\/ACCESS.2021.3052937","volume":"9","author":"N Zhao","year":"2021","unstructured":"Zhao, N., Gao, H., Wen, X., Li, H.: Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access 9, 15561\u201315569 (2021)","journal-title":"IEEE Access"},{"issue":"7","key":"28_CR6","doi-asserted-by":"publisher","first-page":"4408","DOI":"10.1007\/s10489-020-02095-3","volume":"51","author":"Q Lu","year":"2021","unstructured":"Lu, Q., Zhu, Z., Zhang, G., Kang, S., Liu, P.: Aspect-gated graph convolutional networks for aspect-based sentiment analysis. Appl. Intell. 51(7), 4408\u20134419 (2021). https:\/\/doi.org\/10.1007\/s10489-020-02095-3","journal-title":"Appl. Intell."},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Pota, M., Ventura, M., Catelli, R., Esposito, M.: An effective BERT-based pipeline for Twitter sentiment analysis: a case study in Italian. Sensors 21(1), 133 (2021)","DOI":"10.3390\/s21010133"},{"key":"28_CR8","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","volume":"115","author":"ME Basiri","year":"2021","unstructured":"Basiri, M.E., Nemati, S., Abdar, M., Cambria, E., Acharya, U.R.: ABCDE: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur. Gener. Comput. Syst. 115, 279\u2013294 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Srividya, K., Sowjanya, A.M.: NA-DLSTM\u2013A neural attention-based model for context-aware Aspect-based sentiment analysis. Materials Today: Proceedings (2021)","DOI":"10.1016\/j.matpr.2021.01.782"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Dang, N.C., Moreno-Garc\u00eda, M.N., De la Prieta, F.: Sentiment analysis based on deep learning: a comparative study. Electronics 9(3), 483 (2020)","DOI":"10.3390\/electronics9030483"},{"key":"28_CR11","doi-asserted-by":"publisher","first-page":"103180","DOI":"10.1016\/j.compind.2019.103180","volume":"115","author":"I Kandasamy","year":"2020","unstructured":"Kandasamy, I., Vasantha, W.B., Obbineni, J.M., Smarandache, F.: Sentiment analysis of Tweets using refined neutrosophic sets. Comput. Ind. 115, 103180 (2020)","journal-title":"Comput. Ind."},{"key":"28_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cogsys.2018.10.001","volume":"54","author":"ASM Alharbi","year":"2019","unstructured":"Alharbi, A.S.M., de Doncker, E.: Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioural information. Cognit. Syst. Res. 54, 50\u201361 (2019)","journal-title":"Cognit. Syst. Res."},{"key":"28_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.datak.2017.06.001","volume":"114","author":"DH Pham","year":"2018","unstructured":"Pham, D.H., Le, A.C.: Learning multiple layers of knowledge representation for aspect-based sentiment analysis. Data Knowl. Eng. 114, 26\u201339 (2018)","journal-title":"Data Knowl. Eng."},{"key":"28_CR14","unstructured":"Gupta, U., Chatterjee, A., Srikanth, R., Agrawal, P.: A sentiment-and-semantics-based approach for emotion detection in textual conversations (2017). arXiv preprint arXiv:1707.06996"},{"issue":"4","key":"28_CR15","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1253","volume":"8","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(4), e1253 (2018)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Hassan, A., Mahmood, A.: Deep learning approach for sentiment analysis of short texts. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 705\u2013710. IEEE (2017)","DOI":"10.1109\/ICCAR.2017.7942788"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Preethi, G., Krishna, P.V., Obaidat, M.S., Saritha, V., Yenduri, S.: Application of deep learning to sentiment analysis for recommender system on the cloud. In: 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 93\u201397. IEEE (2017)","DOI":"10.1109\/CITS.2017.8035341"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Salas-Z\u00e1rate, M.D.P., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodriguez-Garcia, M.A., Valencia-Garcia, R.: Sentiment analysis on tweets about diabetes: an aspect-level approach. Comput. Math. Methods Med. (2017)","DOI":"10.1155\/2017\/5140631"},{"key":"28_CR19","unstructured":"Ain, Q.T., et al.: Sentiment analysis using deep learning techniques: a review.\u00a0Int. J. Adv. Comput. Sci. Appl. 8(6) (2017)"},{"issue":"12","key":"28_CR20","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1111\/lnc3.12228","volume":"10","author":"LM Rojas-Barahona","year":"2016","unstructured":"Rojas-Barahona, L.M.: Deep learning for sentiment analysis. Lang. Linguist. Compass 10(12), 701\u2013719 (2016)","journal-title":"Lang. Linguist. Compass"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, B., et al.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Tang, D., et al.: Sentiment embeddings with applications to sentiment analysis.\u00a0IEEE Trans. Knowl. Data Eng. 28(2), 496\u2013509 (2015)","DOI":"10.1109\/TKDE.2015.2489653"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959\u2013962 (2015)","DOI":"10.1145\/2766462.2767830"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Exploiting BERT for end-to-end aspect-based sentiment analysis (2019). arXiv preprint arXiv:1910.00883","DOI":"10.18653\/v1\/D19-5505"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Yang, M., et al.: Attention based LSTM for target dependent sentiment classification. In:\u00a0Proceedings of the AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11061"},{"key":"28_CR26","unstructured":"Ma, X., Zhou, C., Yang, X., Huang, Y., Zhu, X.: Modeling sentences with LSTM for emotion detection in textual conversations. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1426\u20131436 (2018)"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: Relational graph attention network for aspect-based sentiment analysis (2020).\u00a0arXiv preprint arXiv:2004.12362","DOI":"10.18653\/v1\/2020.acl-main.295"}],"container-title":["Communications in Computer and Information Science","Advances in Computing and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37940-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T20:25:46Z","timestamp":1690057546000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37940-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031379390","9783031379406"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37940-6_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICACDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing and Data Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icacds2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icacds.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair & Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"464","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}