{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T04:35:20Z","timestamp":1769402120353,"version":"3.49.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03094-8","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T21:03:40Z","timestamp":1722027820000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis"],"prefix":"10.1007","volume":"5","author":[{"given":"S. H. Annie","family":"Silviya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. Julia","family":"Faith","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Seetha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Hemalatha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"3094_CR1","doi-asserted-by":"publisher","first-page":"107056","DOI":"10.1109\/ACCESS.2022.3212367","volume":"10","author":"Y Kit","year":"2022","unstructured":"Kit Y, Mokji MM. Sentiment analysis using pre-trained language model with no fine-tuning and less resource. IEEE Access. 2022;10:107056\u201365. https:\/\/doi.org\/10.1109\/ACCESS.2022.3212367.","journal-title":"IEEE Access"},{"key":"3094_CR2","doi-asserted-by":"publisher","unstructured":"F. T. Saputra, S. H. Wijaya, Y. Nurhadryani and Defina, Lexicon Addition Effect on Lexicon-Based of Indonesian Sentiment Analysis on Twitter. 2020 International Conference on Informatics,Multimedia, Cyber and Information System (ICIMCIS), Jakarta, Indonesia. 2020. pp. 136\u2013141. https:\/\/doi.org\/10.1109\/ICIMCIS51567.2020.9354269.","DOI":"10.1109\/ICIMCIS51567.2020.9354269"},{"key":"3094_CR3","doi-asserted-by":"publisher","unstructured":"K. S. Naveenkumar, R. Vinayakumar and K. P. Soman, Amrita-CEN-SentiDB: Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning; 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 2019. pp. 1522\u20131527, https:\/\/doi.org\/10.1109\/ICCS45141.2019.9065337.","DOI":"10.1109\/ICCS45141.2019.9065337"},{"key":"3094_CR4","doi-asserted-by":"publisher","unstructured":"Tyagi V, Kumar A, Das S. Sentiment analysis on twitter data using deep learning approach, 2020 2nd International Conference on Advances in Computing, Communication Controland Networking (ICACCCN), Greater Noida, India. 2020. pp. 187\u2013190, https:\/\/doi.org\/10.1109\/ICACCCN51052.2020.9362853.","DOI":"10.1109\/ICACCCN51052.2020.9362853"},{"key":"3094_CR5","doi-asserted-by":"publisher","unstructured":"Ibrahim A. Forecasting the early market movement in bitcoin using twitters sentiment analysis: an ensemble-based prediction model, 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, ON, Canada, 2021. pp. 1-5. https:\/\/doi.org\/10.1109\/IEMTRONICS52119.2021.9422647","DOI":"10.1109\/IEMTRONICS52119.2021.9422647"},{"key":"3094_CR6","doi-asserted-by":"publisher","unstructured":"Sintoris K, Vergidis K. Extracting Business Process Models Using Natural Language Processing (NLP) Techniques, 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece. 2017. pp. 135\u2013139. https:\/\/doi.org\/10.1109\/CBI.2017.41.","DOI":"10.1109\/CBI.2017.41"},{"key":"3094_CR7","doi-asserted-by":"publisher","unstructured":"Maass W. How visual salience influences natural language descriptions. IEE Colloquium on Representations: Integration of Sensory Information in Natural Language Processing, Artificial Intelligence and Neural Networks, London, UK. 1995. pp. 3\/1\u20133\/3. https:\/\/doi.org\/10.1049\/ic:19950663.","DOI":"10.1049\/ic:19950663"},{"key":"3094_CR8","doi-asserted-by":"publisher","unstructured":"Tao J, Fang Zheng, Li A, Ya Li. Advances in Chinese Natural Language Processing and Language resources, 2009 Oriental COCOSDA International Conference on Speech Database and Assessments, Urumqi, China. 2009. pp. 13\u201318, https:\/\/doi.org\/10.1109\/ICSDA.2009.5278384.","DOI":"10.1109\/ICSDA.2009.5278384"},{"key":"3094_CR9","doi-asserted-by":"publisher","unstructured":"Peng T, Harris I, Sawa Y. Detecting Phishing Attacks Using Natural Language Processing and Machine Learning, 2018 IEEE 12th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA. 2018. pp. 300\u2013301. https:\/\/doi.org\/10.1109\/ICSC.2018.00056.","DOI":"10.1109\/ICSC.2018.00056"},{"key":"3094_CR10","doi-asserted-by":"publisher","unstructured":"Jing X, Hao Y, Fei H, Li Z. Text encryption algorithm based on natural language processing, 2012 Fourth International Conference on Multimedia Information Networking and Security, Nanjing, China, 2012, pp. 670\u2013672, https:\/\/doi.org\/10.1109\/MINES.2012.216.","DOI":"10.1109\/MINES.2012.216"},{"key":"3094_CR11","doi-asserted-by":"publisher","unstructured":"Karthick S, Victor RJ, Manikandan S, Goswami B. Professional chat application based on natural language processing, 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Bangalore, India. 2018. pp. 1\u20134. https:\/\/doi.org\/10.1109\/ICCTAC.2018.8370395.","DOI":"10.1109\/ICCTAC.2018.8370395"},{"key":"3094_CR12","doi-asserted-by":"publisher","unstructured":"Hachaj T, Ogiela MR. Clusters of trends detection in microblogging: simple natural language processing vs hashtags\u2014which is more informative, 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), Fukuoka, Japan. 2016. pp. 119\u2013121. https:\/\/doi.org\/10.1109\/CISIS.2016.44.","DOI":"10.1109\/CISIS.2016.44"},{"key":"3094_CR13","doi-asserted-by":"publisher","unstructured":"Fatwanto A. Software requirements specification analysis using natural language processing technique, 2013 International Conference on QiR, Yogyakarta, Indonesia. 2013. pp. 105\u2013110. https:\/\/doi.org\/10.1109\/QiR.2013.6632546.","DOI":"10.1109\/QiR.2013.6632546"},{"key":"3094_CR14","doi-asserted-by":"publisher","unstructured":"Shingala A, Virparia P. Enhancing the relevance of information retrieval by querying the database in natural form, 2013 International Conference on Intelligent Systems and Signal Processing(ISSP), Vallabh Vidyanagar, India. 2013. pp. 408\u2013412. https:\/\/doi.org\/10.1109\/ISSP.2013.6526944.","DOI":"10.1109\/ISSP.2013.6526944"},{"key":"3094_CR15","doi-asserted-by":"publisher","unstructured":"Soni M, Gomathi S, Bhupendra Kumar Adhyaru Y. Natural Language Processing for the Job Portal Enhancement, 2020 7th International Conference on Smart Structures and Systems (ICSSS), Chennai, India. 2020. pp. 1\u20134. https:\/\/doi.org\/10.1109\/ICSSS49621.2020.9202046.","DOI":"10.1109\/ICSSS49621.2020.9202046"},{"key":"3094_CR16","doi-asserted-by":"publisher","unstructured":"K\u0142osowski P. Deep learning for natural language processing and language modelling, 2018 signal processing: algorithms, architectures, arrangements, and applications (SPA), Poznan, Poland. 2018. pp. 223-228. https:\/\/doi.org\/10.23919\/SPA.2018.8563389","DOI":"10.23919\/SPA.2018.8563389"},{"issue":"10","key":"3094_CR17","doi-asserted-by":"publisher","first-page":"2950","DOI":"10.1109\/JBHI.2020.2977925","volume":"24","author":"HC Tissot","year":"2020","unstructured":"Tissot HC, et al. Natural language processing for mimicking clinical trial recruitment in critical care: a semi-automated simulation based on the LeoPARDS trial. IEEE J Biomed Health Inform. 2020;24(10):2950\u20139. https:\/\/doi.org\/10.1109\/JBHI.2020.2977925.","journal-title":"IEEE J Biomed Health Inform"},{"key":"3094_CR18","doi-asserted-by":"publisher","unstructured":"Alansary S, Nagi M, Adly N. A suite of tools for Arabic natural language processing: A UNL approach, 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, United Arab Emirates. 2013. pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICCSPA.2013.6487236.","DOI":"10.1109\/ICCSPA.2013.6487236"},{"key":"3094_CR19","doi-asserted-by":"publisher","unstructured":"Das S, Ashrafuzzaman M, Sheldon FT, Shiva S. \u201cNetwork Intrusion Detection using Natural Language Processing and Ensemble Machine Learning,\u201d 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, 2020. pp. 829-835. https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308268","DOI":"10.1109\/SSCI47803.2020.9308268"},{"issue":"4","key":"3094_CR20","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/TITB.2006.884368","volume":"11","author":"RK Taira","year":"2007","unstructured":"Taira RK, Bashyam V, Kangarloo H. A field theoretical approach to medical natural language processing. IEEE Trans Inf Technol Biomed. 2007;11(4):364\u201375. https:\/\/doi.org\/10.1109\/TITB.2006.884368.","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"3094_CR21","doi-asserted-by":"publisher","unstructured":"Khanaferov D, Luc C, Wang T. \u201cSocial Network Data Mining Using Natural Language Processing and Density Based Clustering,\u201d 2014 IEEE International Conference on Semantic Computing, Newport Beach, CA, USA. 2014. pp. 250\u2013251. https:\/\/doi.org\/10.1109\/ICSC.2014.48.","DOI":"10.1109\/ICSC.2014.48"},{"key":"3094_CR22","doi-asserted-by":"publisher","unstructured":"Xing Z, Parandehgheibi M, Xiao F, Kulkarni N, Pouliot C. \u201cContent-based recommendation for podcast audio-items using natural language processing techniques,\u201d 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA. 2016. pp. 2378\u20132383. https:\/\/doi.org\/10.1109\/BigData.2016.7840872.","DOI":"10.1109\/BigData.2016.7840872"},{"key":"3094_CR23","doi-asserted-by":"publisher","unstructured":"Yeo H. A Machine Learning Based Natural Language Question and Answering System for Healthcare Data Search using Complex Queries, \u201c2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA. 2018. pp. 2467\u20132474, https:\/\/doi.org\/10.1109\/BigData.2018.8622448.","DOI":"10.1109\/BigData.2018.8622448"},{"issue":"1","key":"3094_CR24","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/TSMCC.2012.2227472","volume":"44","author":"MT Mills","year":"2014","unstructured":"Mills MT, Bourbakis NG. Graph-based methods for natural language processing and understanding\u2014a survey and analysis. IEEE Trans Syst Man Cybern Syst. 2014;44(1):59\u201371. https:\/\/doi.org\/10.1109\/TSMCC.2012.2227472.","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"3094_CR25","doi-asserted-by":"publisher","unstructured":"Woldemariam Y. \u201cSentiment analysis in a cross-media analysis framework,\u201d 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, China. 2016. pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICBDA.2016.7509790.","DOI":"10.1109\/ICBDA.2016.7509790"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03094-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03094-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03094-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T21:18:38Z","timestamp":1722028718000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03094-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,26]]},"references-count":25,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["3094"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03094-8","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,26]]},"assertion":[{"value":"29 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"The corresponding author can provide access to the dataset generated and analyzed in the current study upon reasonable request<b>.<\/b>","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data Availability"}}],"article-number":"724"}}