{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T10:29:26Z","timestamp":1721557766721},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17391-6","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T08:02:08Z","timestamp":1697529728000},"page":"44217-44250","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quality enhanced hybrid youtube video recommendation based on user preference through sentiment analysis on comments \u2013 a study on natural remedy videos"],"prefix":"10.1007","volume":"83","author":[{"given":"Saravanan","family":"A.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sathya Bama","family":"S.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramila Rajaleximi","family":"P.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anandhi","family":"D.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srividya","family":"M.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"17391_CR1","doi-asserted-by":"publisher","unstructured":"Ac\u0131lar, A. (2022). Health-related internet use among older people in Norway. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2022). https:\/\/doi.org\/10.5220\/0010994800003188","DOI":"10.5220\/0010994800003188"},{"key":"17391_CR2","doi-asserted-by":"publisher","unstructured":"Sri Hari S, Porkodi S, Saranya R, Vijayakumar N. (2022). Intelligent model to improve the efficacy of healthcare content marketing by auto-tagging and exploring the veracity of content using opinion mining. Int J Electron Market Retail (Article in Press). https:\/\/doi.org\/10.1504\/IJEMR.2022.10048222","DOI":"10.1504\/IJEMR.2022.10048222"},{"issue":"2","key":"17391_CR3","doi-asserted-by":"publisher","first-page":"e109","DOI":"10.1016\/j.asmr.2019.09.003","volume":"1","author":"KN Kunze","year":"2019","unstructured":"Kunze KN, Cohn MR, Wakefield C, Hamati F, LaPrade RF, Forsythe B, Yanke AB, Chahla J (2019) YouTube as a source of information about the posterior cruciate ligament: a content-quality and reliability analysis. Arthroscopy, Sports Med, Rehab 1(2):e109\u2013e114. https:\/\/doi.org\/10.1016\/j.asmr.2019.09.003","journal-title":"Arthroscopy, Sports Med, Rehab"},{"issue":"3","key":"17391_CR4","doi-asserted-by":"publisher","DOI":"10.2196\/10831","volume":"21","author":"DKK Wong","year":"2019","unstructured":"Wong DKK, Cheung MK (2019) Online health information seeking and eHealth literacy among patients attending a primary care clinic in Hong Kong: a cross-sectional survey. J Med Internet Res 21(3):e10831. https:\/\/doi.org\/10.2196\/10831","journal-title":"J Med Internet Res"},{"key":"17391_CR5","unstructured":"Brian D (2021) How many people use YouTube in 2022? Backlinko. https:\/\/backlinko.com\/youtube-users. Accessed May 30. 2022"},{"issue":"3","key":"17391_CR6","doi-asserted-by":"publisher","first-page":"201","DOI":"10.17730\/humo.24.3.m85151654737t712","volume":"24","author":"H Gould","year":"1965","unstructured":"Gould H (1965) Modern medicine and folk cognition in rural India. Hum Organ 24(3):201\u2013208. https:\/\/doi.org\/10.17730\/humo.24.3.m85151654737t712","journal-title":"Hum Organ"},{"key":"17391_CR7","unstructured":"Ballani, A., Science behind these common home remedies, the time of India, August 2019. https:\/\/timesofindia.indiatimes.com\/life-style\/health-fitness\/home-remedies\/science-behind-these-common-home-remedies\/articleshow\/51251928.cms. Accessed on May 2022."},{"issue":"2","key":"17391_CR8","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.jtcme.2016.05.006","volume":"7","author":"S Sen","year":"2017","unstructured":"Sen S, Chakraborty R (2017) Revival, modernization and integration of Indian traditional herbal medicine in clinical practice: Importance, challenges and future. J Tradit Complement Med 7(2):234\u2013244. https:\/\/doi.org\/10.1016\/j.jtcme.2016.05.006","journal-title":"J Tradit Complement Med"},{"key":"17391_CR9","unstructured":"Medicalxpress, 2020, Home remedies boom as India pandemic cases soar. https:\/\/medicalxpress.com\/news\/2020-10-home-remedies-boom-india-pandemic.html. Accessed on April 2020."},{"issue":"1","key":"17391_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2296-15-116","volume":"15","author":"LM Parisius","year":"2014","unstructured":"Parisius LM, Stock-Schr\u00f6er B, Berger S, Hermann K, Joos S (2014) Use of home remedies: a cross-sectional survey of patients in Germany. BMC Fam Pract 15(1):1\u20138. https:\/\/doi.org\/10.1186\/1471-2296-15-116","journal-title":"BMC Fam Pract"},{"key":"17391_CR11","doi-asserted-by":"publisher","DOI":"10.7916\/D86M4K9P","volume-title":"A descriptive analysis of the most viewed YouTube videos related to depression","author":"EP Baquero","year":"2018","unstructured":"Baquero EP (2018) A descriptive analysis of the most viewed YouTube videos related to depression (Doctoral dissertation,. Columbia University). https:\/\/doi.org\/10.7916\/D86M4K9P"},{"issue":"2","key":"17391_CR12","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.jvsv.2016.10.078","volume":"5","author":"TM Kwok","year":"2017","unstructured":"Kwok TM, Singla AA, Phang K, Lau AY (2017) YouTube as a source of patient information for varicose vein treatment options. J Vascul Surgery: Venous Lymphatic Disord 5(2):238\u2013243. https:\/\/doi.org\/10.1016\/j.jvsv.2016.10.078","journal-title":"J Vascul Surgery: Venous Lymphatic Disord"},{"issue":"3","key":"17391_CR13","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.euf.2019.09.017","volume":"6","author":"M Farag","year":"2020","unstructured":"Farag M, Bolton D, Lawrentschuk N (2020) Use of YouTube as a resource for surgical education\u2014clarity or confusion. Euro Urol Focus 6(3):445\u2013449. https:\/\/doi.org\/10.1016\/j.euf.2019.09.017","journal-title":"Euro Urol Focus"},{"issue":"1","key":"17391_CR14","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1186\/s12909-022-03446-z","volume":"22","author":"W Osman","year":"2022","unstructured":"Osman W, Mohamed F, Elhassan M, Shoufan A (2022) Is YouTube a reliable source of health-related information? A systematic review. BMC Med Educ 22(1):382. https:\/\/doi.org\/10.1186\/s12909-022-03446-z","journal-title":"BMC Med Educ"},{"issue":"1","key":"17391_CR15","doi-asserted-by":"publisher","DOI":"10.2196\/ijmr.2465","volume":"2","author":"E Gabarron","year":"2013","unstructured":"Gabarron E, Fernandez-Luque L, Armayones M, Lau AY (2013) Identifying measures used for assessing quality of YouTube videos with patient health information: a review of current literature. Int J Med Res 2(1):e2465. https:\/\/doi.org\/10.2196\/ijmr.2465","journal-title":"Int J Med Res"},{"key":"17391_CR16","doi-asserted-by":"publisher","first-page":"205520761769890","DOI":"10.1177\/205520761769890","volume":"3","author":"S Meldrum","year":"2017","unstructured":"Meldrum S, Savarimuthu BT, Licorish S, Tahir A, Bosu M, Jayakaran P (2017) Is knee pain information on YouTube videos perceived to be helpful? An analysis of user comments and implications for dissemination on social media. Digital Health 3:2055207617698908. https:\/\/doi.org\/10.1177\/205520761769890","journal-title":"Digital Health"},{"key":"17391_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2020.103939","volume":"156","author":"I Dubovi","year":"2020","unstructured":"Dubovi I, Tabak I (2020) An empirical analysis of knowledge co-construction in YouTube comments. Comput Educ 156:103939. https:\/\/doi.org\/10.1016\/j.compedu.2020.103939","journal-title":"Comput Educ"},{"key":"17391_CR18","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.procs.2020.10.084","volume":"177","author":"KM Kavitha","year":"2020","unstructured":"Kavitha KM, Shetty A, Abreo B, D\u2019Souza A, Kondana A (2020) Analysis and classification of user comments on YouTube videos. Procedia Comput Sci 177:593\u2013598. https:\/\/doi.org\/10.1016\/j.procs.2020.10.084","journal-title":"Procedia Comput Sci"},{"key":"17391_CR19","first-page":"42","volume":"2013","author":"P Schultes","year":"2013","unstructured":"Schultes P, Dorner V, Lehner F (2013) Leave a comment! An in-depth analysis of user comments on YouTube. Wirtschaftsinformatik Proc 2013:42 https:\/\/aisel.aisnet.org\/wi2013\/42","journal-title":"Wirtschaftsinformatik Proc"},{"key":"17391_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2019.102027","volume":"54","author":"R Ladhari","year":"2020","unstructured":"Ladhari R, Massa E, Skandrani H (2020) YouTube vloggers\u2019 popularity and influence: The roles of homophily, emotional attachment, and expertise. J Retail Consum Serv 54:102027. https:\/\/doi.org\/10.1016\/j.jretconser.2019.102027","journal-title":"J Retail Consum Serv"},{"key":"17391_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104107","volume":"137","author":"MF Aydin","year":"2020","unstructured":"Aydin MF, Aydin MA (2020) Quality and reliability of information available on YouTube and Google pertaining gastroesophageal reflux disease. Int J Med Inform 137:104107. https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104107","journal-title":"Int J Med Inform"},{"key":"17391_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmaid.2020.101636","volume":"35","author":"P Khatri","year":"2020","unstructured":"Khatri P, Singh SR, Belani NK, Yeong YL, Lohan R, Lim YW, Teo WZ (2020) YouTube as source of information on 2019 novel coronavirus outbreak: a cross sectional study of English and Mandarin content. Travel Med Infect Dis 35:101636. https:\/\/doi.org\/10.1016\/j.tmaid.2020.101636","journal-title":"Travel Med Infect Dis"},{"key":"17391_CR23","doi-asserted-by":"publisher","unstructured":"Chawla S, Ding J, Mazhar L, Khosa F (2021). Entering the misinformation age: quality and reliability of youtube for patient information on liposuction. Plastic Surgery, 22925503211064382. https:\/\/doi.org\/10.1177\/22925503211064382","DOI":"10.1177\/22925503211064382"},{"key":"17391_CR24","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.jad.2018.12.119","volume":"246","author":"I Sangeorzan","year":"2019","unstructured":"Sangeorzan I, Andriopoulou P, Livanou M (2019) Exploring the experiences of people vlogging about severe mental illness on YouTube: An interpretative phenomenological analysis. J Affect Disord 246:422\u2013428. https:\/\/doi.org\/10.1016\/j.jad.2018.12.119","journal-title":"J Affect Disord"},{"issue":"8","key":"17391_CR25","doi-asserted-by":"publisher","DOI":"10.1111\/and.14118","volume":"53","author":"MB Duran","year":"2021","unstructured":"Duran MB, Kizilkan Y (2021) Quality analysis of testicular cancer videos on YouTube. Andrologia 53(8):e14118. https:\/\/doi.org\/10.1111\/and.14118","journal-title":"Andrologia"},{"issue":"5","key":"17391_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/rmv.2132","volume":"30","author":"T Szmuda","year":"2020","unstructured":"Szmuda T, Syed MT, Singh A, Ali S, \u00d6zdemir C, S\u0142oniewski P (2020) YouTube as a source of patient information for coronavirus disease (Covid-19): a content-quality and audience engagement analysis. Rev Med Virol 30(5):e2132. https:\/\/doi.org\/10.1002\/rmv.2132","journal-title":"Rev Med Virol"},{"issue":"5","key":"17391_CR27","doi-asserted-by":"publisher","first-page":"899","DOI":"10.3899\/jrheum.111114","volume":"39","author":"AG Singh","year":"2012","unstructured":"Singh AG, Singh S, Singh PP (2012) YouTube for information on rheumatoid arthritis\u2014a wakeup call? J Rheumatol 39(5):899\u2013903. https:\/\/doi.org\/10.3899\/jrheum.111114","journal-title":"J Rheumatol"},{"issue":"7","key":"17391_CR28","doi-asserted-by":"publisher","DOI":"10.2196\/24994","volume":"23","author":"KN Lee","year":"2021","unstructured":"Lee KN, Joo YJ, Choi SY, Park ST, Lee KY, Kim Y, Son GH (2021) Content analysis and quality evaluation of cesarean delivery\u2013related videos on YouTube: cross-sectional study. J Med Internet Res 23(7):e24994. https:\/\/doi.org\/10.2196\/24994","journal-title":"J Med Internet Res"},{"issue":"3","key":"17391_CR29","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1111\/j.1460-2466.2010.01488.x","volume":"60","author":"MJ Metzger","year":"2010","unstructured":"Metzger MJ, Flanagin AJ, Medders RB (2010) Social and heuristic approaches to credibility evaluation online. J Commun 60(3):413\u2013439. https:\/\/doi.org\/10.1111\/j.1460-2466.2010.01488.x","journal-title":"J Commun"},{"key":"17391_CR30","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/ICCCIS48478.2019.8974514","volume-title":"In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","author":"GS Kalra","year":"2019","unstructured":"Kalra GS, Kathuria RS, Kumar A (2019) YouTube video classification based on title and description text. In: In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, pp 74\u201379. https:\/\/doi.org\/10.1109\/ICCCIS48478.2019.8974514"},{"issue":"9","key":"17391_CR31","first-page":"8030","volume":"3","author":"A Kaur","year":"2014","unstructured":"Kaur A, Singh P, Rani S (2014) Spell checking and error correcting system for text paragraphs written in Punjabi language using hybrid approach. Int Eng Comput Sci 3(9):8030\u20138032","journal-title":"Int Eng Comput Sci"},{"issue":"2","key":"17391_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0171649","volume":"12","author":"MZ Asghar","year":"2017","unstructured":"Asghar MZ, Khan A, Ahmad S, Qasim M, Khan IA (2017) Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLoS One 12(2):e0171649. https:\/\/doi.org\/10.1371\/journal.pone.0171649","journal-title":"PLoS One"},{"key":"17391_CR33","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-25343-5_3","volume-title":"Prominent feature extraction for sentiment analysis, Socio-Affective Computing","author":"B Agarwal","year":"2016","unstructured":"Agarwal B, Mittal N, Agarwal B, Mittal N (2016) Machine learning approach for sentiment analysis. In: Prominent feature extraction for sentiment analysis, Socio-Affective Computing. Springer, Cham, pp 21\u201345. https:\/\/doi.org\/10.1007\/978-3-319-25343-5_3"},{"key":"17391_CR34","doi-asserted-by":"publisher","unstructured":"Thelwall M (2014). Heart and soul: Sentiment strength detection in the social web with sentistrength, 2017. Cyberemotions: Understanding Complex Systems, Springer, Cham, 1-14. https:\/\/doi.org\/10.1007\/978-3-319-43639-5_7","DOI":"10.1007\/978-3-319-43639-5_7"},{"key":"17391_CR35","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.knosys.2016.05.032","volume":"108","author":"A Muhammad","year":"2016","unstructured":"Muhammad A, Wiratunga N, Lothian R (2016) Contextual sentiment analysis for social media genres. Knowl-Based Syst 108:92\u2013101. https:\/\/doi.org\/10.1016\/j.knosys.2016.05.032","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"17391_CR36","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/COLI_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267\u2013307. https:\/\/doi.org\/10.1162\/COLI_a_00049","journal-title":"Comput Linguist"},{"key":"17391_CR37","doi-asserted-by":"publisher","unstructured":"Siersdorfer, S., Chelaru, S., Nejdl, W., & San Pedro, J. (2010). How useful are your comments? Analyzing and predicting YouTube comments and comment ratings. In Proceedings of the 19th international conference on World wide web (pp. 891-900). https:\/\/doi.org\/10.1145\/1772690.1772781","DOI":"10.1145\/1772690.1772781"},{"key":"17391_CR38","doi-asserted-by":"publisher","unstructured":"Cunha, A. A. L., Costa, M. C., & Pacheco, M. A. C. (2019). Sentiment analysis of youtube video comments using deep neural networks. In Artificial Intelligence and Soft Computing: 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16\u201320, 2019, Proceedings, Part I 18 (pp. 561-570). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-20912-4_51","DOI":"10.1007\/978-3-030-20912-4_51"},{"key":"17391_CR39","doi-asserted-by":"publisher","unstructured":"Muhammad, A. N., Bukhori, S., & Pandunata, P. (2019). Sentiment analysis of positive and negative of youtube comments using na\u00efve bayes\u2013support vector machine (nbsvm) classifier. In 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE) (pp. 199-205). IEEE. https:\/\/doi.org\/10.1109\/ICOMITEE.2019.8920923","DOI":"10.1109\/ICOMITEE.2019.8920923"},{"key":"17391_CR40","doi-asserted-by":"publisher","unstructured":"Aufar, M., Andreswari, R., & Pramesti, D. (2020) Sentiment analysis on youtube social media using decision tree and random forest algorithm: A case study. In: 2020 International Conference on Data Science and Its Applications (ICoDSA) (pp. 1-7). IEEE. https:\/\/doi.org\/10.1109\/ICoDSA50139.2020.9213078","DOI":"10.1109\/ICoDSA50139.2020.9213078"},{"key":"17391_CR41","doi-asserted-by":"publisher","unstructured":"Davidson J, Liebald B, Liu J, Nandy P, Van Vleet T, Gargi U, Gupta S, He Y, Lambert M, Livingston B, Sampath D (2010). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems (pp. 293-296). https:\/\/doi.org\/10.1145\/1864708.1864770","DOI":"10.1145\/1864708.1864770"},{"key":"17391_CR42","doi-asserted-by":"publisher","unstructured":"Yan M, Sang J, Xu C (2015). Unified youtube video recommendation via cross-network collaboration. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, Shanghai China (pp. 19-26). https:\/\/doi.org\/10.1145\/2671188.2749344","DOI":"10.1145\/2671188.2749344"},{"key":"17391_CR43","doi-asserted-by":"publisher","unstructured":"Covington P, Adams J, Sargin E (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 191-198). https:\/\/doi.org\/10.1145\/2959100.2959190","DOI":"10.1145\/2959100.2959190"},{"key":"17391_CR44","doi-asserted-by":"publisher","unstructured":"Liu Q, Xie R, Chen L, Liu S, Tu K, Cui P, Zhang B, Lin L (2020) Graph neural network for tag ranking in tag-enhanced video recommendation. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 2613-2620). https:\/\/doi.org\/10.1145\/3340531.3416021","DOI":"10.1145\/3340531.3416021"},{"key":"17391_CR45","doi-asserted-by":"publisher","unstructured":"Wilhelm M, Ramanathan A, Bonomo A, Jain S, Chi EH, Gillenwater J (2018) Practical diversified recommendations on youtube with determinantal point processes. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 2165-2173). https:\/\/doi.org\/10.1145\/3269206.327201839","DOI":"10.1145\/3269206.327201839"},{"key":"17391_CR46","doi-asserted-by":"publisher","unstructured":"Liu S, Chen Z, Liu H, Hu X (2019) User-video co-attention network for personalized micro-video recommendation. In The world wide web conference, San Francisco (pp. 3020-3026). https:\/\/doi.org\/10.1145\/3308558.3313513","DOI":"10.1145\/3308558.3313513"},{"issue":"1","key":"17391_CR47","doi-asserted-by":"publisher","first-page":"239","DOI":"10.32604\/cmes.2022.022827","volume":"135","author":"W Liu","year":"2023","unstructured":"Liu W, Wan H, Yan B (2023) Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context. CMES-Comput Model Eng Sci 135(1):239\u2013258. https:\/\/doi.org\/10.32604\/cmes.2022.022827","journal-title":"CMES-Comput Model Eng Sci"},{"key":"17391_CR48","doi-asserted-by":"publisher","first-page":"6954","DOI":"10.1109\/ACCESS.2019.2961392","volume":"8","author":"R Zhou","year":"2019","unstructured":"Zhou R, Xia D, Wan J, Zhang S (2019) An intelligent video tag recommendation method for improving video popularity in mobile computing environment. IEEE Access 8:6954\u20136967. https:\/\/doi.org\/10.1109\/ACCESS.2019.2961392","journal-title":"IEEE Access"},{"key":"17391_CR49","doi-asserted-by":"publisher","unstructured":"Dokuz Y (2023) Discovering popular and persistent tags from YouTube trending video big dataset. Multimed Tools Appl 1-19. https:\/\/doi.org\/10.1007\/s11042-023-16019-z","DOI":"10.1007\/s11042-023-16019-z"},{"issue":"3","key":"17391_CR50","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.3906\/elk-1907-46","volume":"28","author":"M I\u015fik","year":"2020","unstructured":"I\u015fik M, Da\u011f H (2020) The impact of text preprocessing on the prediction of review ratings. Turk J Electr Eng Comput Sci 28(3):1405\u20131421. https:\/\/doi.org\/10.3906\/elk-1907-46","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"17391_CR51","doi-asserted-by":"crossref","unstructured":"Anandarajan M, Hill C, Nolan T (2019) Practical text analytics. Maximizing the Value of Text Data. (Advances in Analytics and Data Science. Vol. 2.) Springer, 45-59. https:\/\/link.springer.com\/book\/10.1007\/978-3-319-95663-3","DOI":"10.1007\/978-3-319-95663-3_4"},{"key":"17391_CR52","doi-asserted-by":"publisher","unstructured":"Virmani D, Taneja S (2019) A text preprocessing approach for efficacious information retrieval. In Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS 2017, Volume 1 (pp. 13-22). Springer Singapore. https:\/\/doi.org\/10.1007\/978-981-10-8968-8_2","DOI":"10.1007\/978-981-10-8968-8_2"},{"issue":"3","key":"17391_CR53","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1108\/00330330610681286","volume":"40","author":"MF Porter","year":"2006","unstructured":"Porter MF (2006) An algorithm for suffix stripping. Program 40(3):211\u2013218. https:\/\/doi.org\/10.1108\/00330330610681286","journal-title":"Program"},{"issue":"5","key":"17391_CR54","first-page":"13625","volume":"10","author":"SS Sathya Bama","year":"2015","unstructured":"Sathya Bama SS, Ahmed MI, Saravanan A (2015) Enhancing the search engine results through web content ranking. Int J Appl Eng Res 10(5):13625\u201313635","journal-title":"Int J Appl Eng Res"},{"issue":"2","key":"17391_CR55","first-page":"343","volume":"60","author":"S Sathya Bama","year":"2014","unstructured":"Sathya Bama S, Ahmed I, Saravanan A (2014) A Mathematical Approach for Improving the Performance of the Search Engine through Web Content Mining. J Theoret Appl Informat Technol 60(2):343\u2013350 http:\/\/www.jatit.org\/volumes\/Vol60No2\/18Vol60No2.pdf","journal-title":"J Theoret Appl Informat Technol"},{"key":"17391_CR56","doi-asserted-by":"crossref","unstructured":"Abbas SM (2017) Improved context-aware youtube recommender system with user feedback analysis. Bahria Univ J Inform Commun Technol (BUJICT) 10(2)","DOI":"10.1109\/ICICT.2017.8320183"},{"key":"17391_CR57","unstructured":"Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Lrec (Vol. 10, No. 2010, pp. 2200-2204). http:\/\/nmis.isti.cnr.it\/sebastiani\/Publications\/LREC10.pdf, 10:2200\u20132204"},{"issue":"8","key":"17391_CR58","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1108\/IMDS-07-2017-0300","volume":"118","author":"W Kaur","year":"2018","unstructured":"Kaur W, Balakrishnan V (2018) Improving sentiment scoring mechanism: a case study on airline services. Ind Manag Data Syst 118(8):1578\u20131596. https:\/\/doi.org\/10.1108\/IMDS-07-2017-0300","journal-title":"Ind Manag Data Syst"},{"key":"17391_CR59","unstructured":"Nazma M (2018) Sentiment analysis on twitter data using different algorithms. North Dakota State Univ https:\/\/hdl.handle.net\/10365\/29114"},{"key":"17391_CR60","unstructured":"Vryniotis V (2013) Machine learning tutorial: The max entropy text classifier. Mach learn blog software develop news. https:\/\/blog.datumbox.com\/machine-learning-tutorial-the-max-entropy-text-classifier\/"},{"issue":"1S","key":"17391_CR61","first-page":"695","volume":"7","author":"H Htet","year":"2018","unstructured":"Htet H, Myint YY (2018) Social media (Twitter) Data analysis using maximum entropy classifier on big data processing framework (Case study: Analysis of health condition, education status, states of business). J Pharmacog Phytochem 7(1S):695\u2013700","journal-title":"J Pharmacog Phytochem"},{"key":"17391_CR62","first-page":"816","volume-title":"European Conference on Information Retrieval","author":"A Yates","year":"2013","unstructured":"Yates A, Goharian N (2013) ADRTrace: detecting expected and unexpected adverse drug reactions from user reviews on social media sites. In: European Conference on Information Retrieval. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 816\u2013819. http:\/\/ir.cs.georgetown.edu\/data\/adr\/"},{"key":"17391_CR63","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s10791-011-9174-8","volume":"15","author":"K Ganesan","year":"2012","unstructured":"Ganesan K, Zhai C (2012) Opinion-based entity ranking. Inf Retr 15:116\u2013150. https:\/\/archive.ics.uci.edu\/ml\/datasets\/OpinRank+Review+Dataset","journal-title":"Inf Retr"},{"issue":"4","key":"17391_CR64","first-page":"285","volume":"4","author":"P Kalaivani","year":"2013","unstructured":"Kalaivani P, Shunmuganathan KL (2013) Sentiment classification of movie reviews by supervised machine learning approaches. Indian J Comput Sci Engin 4(4):285\u2013292. http:\/\/www.ijcse.com\/docs\/INDJCSE13-04-04-034.pdf","journal-title":"Indian J Comput Sci Engin"},{"issue":"6","key":"17391_CR65","first-page":"238","volume":"4","author":"FM Kundi","year":"2014","unstructured":"Kundi FM, Khan A, Ahmad S, Asghar MZ (2014) Lexicon-based sentiment analysis in the social web. J Basic Appl Scient Res 4(6):238\u2013248","journal-title":"J Basic Appl Scient Res"},{"issue":"9","key":"17391_CR66","first-page":"66","volume":"11","author":"FM Kundi","year":"2014","unstructured":"Kundi FM, Ahmad S, Khan A, Asghar MZ (2014a) Detection and scoring of internet slangs for sentiment analysis using SentiWordNet. Life Sci J 11(9):66\u201372","journal-title":"Life Sci J"},{"key":"17391_CR67","unstructured":"Sanders N, 2013. Twitter Sentiment Analysis Dataset. http:\/\/www.sananalytics.com\/lab\/twitter-sentiment\/"},{"key":"17391_CR68","volume-title":"Data Mining: Concepts and","author":"J Han","year":"2012","unstructured":"Han J, Kamber M, Pei J (2012) Data Mining: Concepts and. Morgan Kaufmann Publishers, Techniques, Waltham"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17391-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17391-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17391-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T11:27:54Z","timestamp":1714390074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17391-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,17]]},"references-count":68,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17391"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17391-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,17]]},"assertion":[{"value":"14 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}