{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:04:02Z","timestamp":1774631042675,"version":"3.50.1"},"reference-count":21,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T00:00:00Z","timestamp":1616803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,11,17]]},"abstract":"<jats:p>The decisions and approaches of renowned personality used to impress the real world are to a great extent adapted to how others have seen or assessed the world with opinion and sentiment. Examples could be any opinion and sentiment of people view about Movie audits, Movie surveys, web journals, smaller scale websites, and informal organizations. In this research classifies the movie review into its correct category, classifier model is proposed that has been trained by applying feature extraction and feature ranking. The focus is on how to examine the sentiment expression and classification of a given movie review on a scale of (\u2013) negative and (+) positive sentiments analysis for the IMDB movie review database. Due to the lack of grammatical structures to comments on movies, natural language processing (NLP) has been used to implement proposed model and experimentation is performed to compare the present study with existing learning models. At the outset, our approach to sentiment classification supplements the existing movie rating systems used across the web to an accuracy of 97.68%.<\/jats:p>","DOI":"10.3233\/jifs-189866","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T14:37:24Z","timestamp":1617115044000},"page":"5449-5456","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Analysis of sentiment based movie reviews using machine learning techniques"],"prefix":"10.1177","volume":"41","author":[{"given":"Sachin","family":"Chirgaiya","sequence":"first","affiliation":[{"name":"SVVV, Indore, Vnrvjiet, Hyderabad, IMS DAVV Indore and SVVV, Indore, India"}]},{"given":"Deepak","family":"Sukheja","sequence":"additional","affiliation":[{"name":"SVVV, Indore, Vnrvjiet, Hyderabad, IMS DAVV Indore and SVVV, Indore, India"}]},{"given":"Niranjan","family":"Shrivastava","sequence":"additional","affiliation":[{"name":"SVVV, Indore, Vnrvjiet, Hyderabad, IMS DAVV Indore and SVVV, Indore, India"}]},{"given":"Romil","family":"Rawat","sequence":"additional","affiliation":[{"name":"SVVV, Indore, Vnrvjiet, Hyderabad, IMS DAVV Indore and SVVV, Indore, India"}]}],"member":"179","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"PangB. LeeL. and VaithyanathanS. Thumbs up?: sentiment classification using machine learning techniques in Association for Computational Linguistics (2002) pp. 79\u201386.","DOI":"10.3115\/1118693.1118704"},{"key":"e_1_3_1_3_2","unstructured":"LiuB. Sentiment analysis and subjectivity handbook of natural language processing 2nd edn (2010)."},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"LiuB. \u201cSentiment Analysis and Opinion Mining\u201d \u201cSynthesis Lectures on Human Language Technologies\u201d (2012).","DOI":"10.1007\/978-3-031-02145-9"},{"key":"e_1_3_1_5_2","unstructured":"LiuB. \u201cSentiment Analysis and Opinion Mining\u201d Morgan & Claypool Publishers (2012)."},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"LiW. and ChenH. Identifying top sellers in underground economy using deep learning-based sentiment analysis. In: IEEE joint intelligence and security informatics conference (2014) pp. 64\u20137.","DOI":"10.1109\/JISIC.2014.19"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.4304\/jsw.9.8.2065-2072"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"LiuB. \u201cSentiment Analysis: mining sentiments opinions and emotions\u201dCambridge University Press (2015).","DOI":"10.1017\/CBO9781139084789"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.08.027"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"CatalC. and NangirM. A sentiment classification model based on multiple classifiers Appl. Soft Comput. (2016) 135\u2013141.","DOI":"10.1016\/j.asoc.2016.11.022"},{"key":"e_1_3_1_11_2","unstructured":"BrownleeJason \u201cHow to Prepare Movie Review Data for Sentiment Analysis\u201d in Natural Language Processing (2017)."},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"WankhedeR. and ThakareA. Design approach for accuracy in movie reviews using sentiment analysis IEEE Xplore: (2017).","DOI":"10.1109\/ICECA.2017.8203652"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"AkhtarS. GuptaD. EkbalA. and BhattacharyyaP. Feature selection and ensemble construction: a two-step method for aspect-based sentiment analysis Knowl. Based on Syst. (2017).","DOI":"10.1016\/j.knosys.2017.03.020"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"ZhangS. WeiZ. WangY. and LiaoT. Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary Future Gen. Comput. Syst (2017).","DOI":"10.1016\/j.future.2017.09.048"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"AraqueO. Corcuera-platasI. S\u00e1nchez-radaJ.F. and IglesiasC.A. Enhancing deep learning sentiment analysis with ensemble techniques in social applications Expert Syst. Appl. (2017).","DOI":"10.1016\/j.eswa.2017.02.002"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"YenterA. and VermaA. Deep CNN-LSTM with Combined Kernels from Multiple Branches for IMDb Review Sentiment Analysis IEEE International Conference on Computer Communication and the Internet (2017).","DOI":"10.1109\/UEMCON.2017.8249013"},{"issue":"4","key":"e_1_3_1_17_2","first-page":"254","article-title":"Sentiment analysis using ensemble learners","volume":"12","author":"Iqbal F.","year":"2018","unstructured":"IqbalF., Sentiment analysis using ensemble learners, Int. J. Comput. Eng. Appl.12(4) (2018), 254\u2013259.","journal-title":"Int. J. Comput. Eng. Appl."},{"key":"e_1_3_1_18_2","unstructured":"UKEssays. Sentiment Analysis of Movie Reviews Using SentiWordNet https:\/\/www.ukessays.com\/essays\/film-studies\/sentiment-analysis-of-moviereviews-using-sentiwordnet.php?vref=1. Last accessed 2020\/01\/21."},{"key":"e_1_3_1_19_2","unstructured":"PouransariH. and GhiliS. Deep learning for sentiment analysis of movie reviews https:\/\/www.kaggle.com last accessed 2020\/01\/21."},{"issue":"2","key":"e_1_3_1_20_2","article-title":"A survey on review analysis using deep learning techniques","volume":"4","author":"Sajeevan A.","year":"2019","unstructured":"SajeevanA., A survey on review analysis using deep learning techniques, International Journal of Latest Engineering and Management Research (IJLEMR)4(2) (2019).","journal-title":"International Journal of Latest Engineering and Management Research (IJLEMR)"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-2354-6_34"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-2354-6_12"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189866","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-189866","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189866","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:51:19Z","timestamp":1769993479000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-189866"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,27]]},"references-count":21,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,11,17]]}},"alternative-id":["10.3233\/JIFS-189866"],"URL":"https:\/\/doi.org\/10.3233\/jifs-189866","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,27]]}}}