{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:46:48Z","timestamp":1775209608429,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031398100","type":"print"},{"value":"9783031398117","type":"electronic"}],"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\/978-3-031-39811-7_11","type":"book-chapter","created":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T18:01:28Z","timestamp":1693159288000},"page":"128-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecast of Movie Sentiment Based on Multi Label Text Classification on Rotten Tomatoes Using Multiple Machine and Deep Learning Technique"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9792-5610","authenticated-orcid":false,"given":"Debarati","family":"Nath","sequence":"first","affiliation":[]},{"given":"Joseph","family":"Roy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Suhariyanto, Firmanto, A., Sarno, R.: Prediction of movie sentiment based on reviews and score on rotten tomatoes using sentiwordnet. In: 2018 International Seminar on Application for Technology of Information and Communication, pp. 202\u2013206, IEEE Indonesia (2018). https:\/\/doi.org\/10.1109\/ISEMANTIC.2018.8549704","DOI":"10.1109\/ISEMANTIC.2018.8549704"},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"30","DOI":"10.4018\/IJKDB.2017010103","volume":"7","author":"P Tiwari","year":"2017","unstructured":"Tiwari, P., Mishra, B.K.K., Kumar, S., Kumar, V.: Implementation of n-gram methodology for rotten tomatoes review dataset sentiment analysis. Int. J. Knowl. Discov. Bioinform. 7(1), 30\u201340 (2017). https:\/\/doi.org\/10.4018\/IJKDB.2017010103","journal-title":"Int. J. Knowl. Discov. Bioinform."},{"key":"11_CR3","unstructured":"Jain, S., Shrikant, M., Mishra, R., Tiwary, U.S.: Sentiment analysis: an empirical comparative study of various machine learning approaches. In: Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017), pp. 112\u2013121, NLP Association of India, Kolkata, India (2017). https:\/\/aclanthology.org\/W17-7515"},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"Wu, J.Y., Pao, Y.: Predicting sentiment from rotten tomatoes movie reviews (2012). https:\/\/nlp.stanford.edu\/courses\/cs224n\/2012\/reports\/WuJean_PaoYuanyuan_224nReport.pdf https:\/\/doi.org\/10.1109\/ISEMANTIC.2018.8549704","DOI":"10.1109\/ISEMANTIC.2018.8549704"},{"issue":"4","key":"11_CR5","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1016\/j.dss.2012.12.022","volume":"55","author":"R Huaxia","year":"2013","unstructured":"Huaxia, R., Yizao, L., Andrew, W.: Whose and what chatter matters? the effect of tweets on movie sales. Decis. Support Syst. 55(4), 863\u2013870 (2013). https:\/\/doi.org\/10.1016\/j.dss.2012.12.022","journal-title":"Decis. Support Syst."},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Sankar, H., Subramaniyaswamy, V.: Investigating sentiment analysis using machine learning approach. In: International Conference on Intelligent Sustainable Systems (ICISS), IEEE, pp. 87\u201392, December 7\u20138, (2017). https:\/\/doi.org\/10.1109\/ISS1.2017.8389293","DOI":"10.1109\/ISS1.2017.8389293"},{"issue":"4","key":"11_CR7","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1109\/ISS1.2017.8389293","volume":"5","author":"M Walaa","year":"2014","unstructured":"Walaa, M., Ahmed, H., Hoda, K.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093\u20131113 (2014). https:\/\/doi.org\/10.1109\/ISS1.2017.8389293","journal-title":"Ain Shams Eng. J."},{"key":"11_CR8","unstructured":"Yao, Y., Angelov, I., Rasmus-Vorrath, J., Lee, M., Engels, Daniel, W.: Yelp\u2019s review filtering algorithm, SMU Data Science Review: 1(3) , Article 3 (2018). https:\/\/scholar.smu.edu\/datasciencereview\/vol1\/iss3\/3"},{"issue":"3","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ISCMI.2014.16","volume":"5","author":"S Onalaja","year":"2021","unstructured":"Onalaja, S., Romero, E., Yun, B.: Aspect-based sentiment analysis of movie reviews. SMU Data Sci. Rev. 5(3), 1\u201321 (2021). https:\/\/doi.org\/10.1109\/ISCMI.2014.16","journal-title":"SMU Data Sci. Rev."},{"key":"11_CR10","doi-asserted-by":"publisher","unstructured":"Pang, B., Lee, L., Vaithyanathan, S.: {T}humbs up? {S}entiment classification using machine learning techniques, In: Proceeding of the EMNLP\u201902, (2002). https:\/\/doi.org\/10.3115\/1118693.1118704","DOI":"10.3115\/1118693.1118704"},{"key":"11_CR11","doi-asserted-by":"publisher","unstructured":"Bo, P., Lillian, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, pp.115\u2013124, (2005). https:\/\/doi.org\/10.3115\/1219840.1219855","DOI":"10.3115\/1219840.1219855"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Salvetti, F., Lewis, S., Cheng, X.: Automtatic opinion polarity classification of movie. Colorado Res. Linguist. 17(2), (2009). https:\/\/doi.org\/10.25810\/atv1-v819","DOI":"10.25810\/atv1-v819"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Matsumoto, D., Hwang, H.C.: Assessing cross-cultural competence: a review of available tests. J. Cross-cult. Psychol. 44(6), 849\u2013873 (2013). https:\/\/doi.org\/10.1177\/0022022113492891","DOI":"10.1177\/0022022113492891"},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, pp. 271\u2013278, (2004). https:\/\/doi.org\/10.3115\/1218955.1218990","DOI":"10.3115\/1218955.1218990"},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.procs.2013.05.005","volume":"17","author":"H Emma","year":"2013","unstructured":"Emma, H., Xiaohui, L., Yong, S.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26\u201332 (2013). https:\/\/doi.org\/10.1016\/j.procs.2013.05.005","journal-title":"Procedia Comput. Sci."},{"key":"11_CR16","unstructured":"OKeefe, T., Koprinska, I.: Feature selection and weighting methods in sentiment analysis. In: Proceedings of the 14th Australasian Document Computing Symposium, Sydney. Citeseer, pp.67\u201374, (2009)"},{"issue":"1","key":"11_CR17","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3233\/JIFS-191171","volume":"39","author":"Y Shujuan","year":"2020","unstructured":"Shujuan, Y., Danlei, L., Wenfeng, Z., Yun, Z., Shengmei, Z.: Attention-based LSTM. J. Intell. Fuzzy Syst.: Appl. Eng. Technol. 39(1), 333\u2013340 (2020). https:\/\/doi.org\/10.3233\/JIFS-191171","journal-title":"J. Intell. Fuzzy Syst.: Appl. Eng. Technol."},{"key":"11_CR18","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.252","volume":"6","author":"MN Al","year":"2020","unstructured":"Al, M.N., McGough, S., Awwad, S.H.B.: Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. Peer J. Comput. Sci. 6, e252 (2020). https:\/\/doi.org\/10.7717\/peerj-cs.252","journal-title":"Peer J. Comput. Sci."},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Yoon, K.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp.1746\u20141751 (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1181","DOI":"10.3115\/v1\/D14-1181"}],"container-title":["IFIP Advances in Information and Communication Technology","Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39811-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T12:02:30Z","timestamp":1698580950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39811-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031398100","9783031398117"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39811-7_11","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"28 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer, Communication, and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chennai","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":"4 January 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 January 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":"icccsp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icccsp.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"123","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":"17","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":"9","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":"14% - 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":"4","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)"}}]}}