{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:44:01Z","timestamp":1777704241324,"version":"3.51.4"},"reference-count":11,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,4,12]]},"abstract":"<jats:p>Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.<\/jats:p>","DOI":"10.3233\/jifs-189478","type":"journal-article","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T12:36:53Z","timestamp":1607690213000},"page":"6375-6383","source":"Crossref","is-referenced-by-count":86,"title":["Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms"],"prefix":"10.1177","volume":"40","author":[{"given":"P.","family":"Ajitha","sequence":"first","affiliation":[{"name":"School of Computing, Sathyabama Institute of Science and Technology, Chennai, India"}]},{"given":"A.","family":"Sivasangari","sequence":"additional","affiliation":[{"name":"School of Computing, Sathyabama Institute of Science and Technology, Chennai, India"}]},{"given":"R.","family":"Immanuel Rajkumar","sequence":"additional","affiliation":[{"name":"Sathyabama Institute of Science and Technology, Chennai, India"}]},{"given":"S.","family":"Poonguzhali","sequence":"additional","affiliation":[{"name":"Sathyabama Institute of Science and Technology, Chennai, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189478_ref1","doi-asserted-by":"crossref","unstructured":"Fang Y. , Tan H. and Zhang A.J. , Multistrategy Sentiment Analysis Of Consumer Reviews Based On Semantic Fuzziness, Preceding In IEEE 6 (2018).","DOI":"10.1109\/ACCESS.2018.2820025"},{"key":"10.3233\/JIFS-189478_ref2","doi-asserted-by":"crossref","unstructured":"Jianqiang Z. , Xiaolin G. and Xuejun A.Z. , Deep Convolution Neural Networks For Twitter Sentiment Analysis, Preceding In Ieee 6 (2018).","DOI":"10.1109\/ACCESS.2017.2776930"},{"key":"10.3233\/JIFS-189478_ref3","doi-asserted-by":"crossref","unstructured":"Jianqiang Z. and Xiaolin G. , Comparison Research On Text Pre-Processing Methods On Twitter Sentiment Analysis, 5 (2017).","DOI":"10.1109\/ACCESS.2017.2672677"},{"key":"10.3233\/JIFS-189478_ref4","doi-asserted-by":"crossref","unstructured":"Bouazizi M. and Ohtsuki T. , A Pattern-Based Approach For Multi-Class Sentiment Analysis In Twitter, Preceding In Ieee 5 (2017).","DOI":"10.1109\/ACCESS.2017.2740982"},{"key":"10.3233\/JIFS-189478_ref5","doi-asserted-by":"crossref","unstructured":"Al-Moslmi T. , Omar N. , Abdullah S. and Albared M. , Approaches To Cross-Domain Sentiment Analysis: A Systematic Literature Review, Preceding In Ieee 5 (2017).","DOI":"10.1109\/ACCESS.2017.2690342"},{"key":"10.3233\/JIFS-189478_ref6","doi-asserted-by":"crossref","unstructured":"Lei X. , Qian X. , Member Ieee and Zhao G. , Rating Prediction Based On Social Sentiment From Textual Reviews, Preceding In Ieee 18(9) (2016).","DOI":"10.1109\/TMM.2016.2575738"},{"key":"10.3233\/JIFS-189478_ref9","doi-asserted-by":"crossref","unstructured":"Xia R. , Xu F. , Zong C. , Li Q. , Qi Y. and Li T. , Dual Sentiment Analysis: Considering Two Sides Of One Review, Preceding In Ieee Transactions On Knowledge And Data Engineering 27(8) (2015).","DOI":"10.1109\/TKDE.2015.2407371"},{"issue":"1","key":"10.3233\/JIFS-189478_ref12","first-page":"7","article-title":"An Analysis Of Opinion Mining Research Works Based On Language, Writing Style And Feature Selection Parameters","volume":"1","author":"Kaur","year":"2009","journal-title":"International Journal Of Advanced Networking Applications (Ijana)"},{"issue":"2S","key":"10.3233\/JIFS-189478_ref13","first-page":"1370","article-title":"Air Pollution Monitoring and Prediction using Multi view Hybrid Model","volume":"8","author":"Sivasangari","year":"2019","journal-title":"International Journal of Engineering and Advanced Technology(IJEAT)"},{"issue":"3","key":"10.3233\/JIFS-189478_ref14","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1166\/jmihi.2016.1756","article-title":"Semantic Based Fuzzy Inference System(SBFIS) Prediction of Patient Emotion and Prescription using support vector machine\u201d","volume":"6","author":"Ajitha","year":"2016","journal-title":"the Journal of Medical Imaging and Health Informatics"},{"key":"10.3233\/JIFS-189478_ref17","doi-asserted-by":"crossref","first-page":"103180","DOI":"10.1016\/j.compind.2019.103180","article-title":"Sentiment analysis of tweets using refined neutrosophic sets","volume":"115","author":"Kandasamy","year":"2020","journal-title":"Computers in Industry"}],"container-title":["Journal of Intelligent &amp; 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