{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:01:41Z","timestamp":1781107301004,"version":"3.54.1"},"reference-count":15,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7]]},"abstract":"<jats:p>Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Na\u00efve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.<\/jats:p>","DOI":"10.4018\/ijdsst.2019070101","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T06:52:37Z","timestamp":1559026357000},"page":"1-12","source":"Crossref","is-referenced-by-count":9,"title":["Investigating Machine Learning Techniques for User Sentiment Analysis"],"prefix":"10.4018","volume":"11","author":[{"given":"Nimesh V","family":"Patel","sequence":"first","affiliation":[{"name":"C.U Shah University, Wadhawan (Surendranagar-Gujarat), India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitesh","family":"Chhinkaniwala","sequence":"additional","affiliation":[{"name":"Adani Institute of Infrastructure Engineering, Gujarat, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"issue":"6","key":"IJDSST.2019070101-0","first-page":"1","article-title":"Twitter sentiment analysis of movie reviews using machine learning techniques.","volume":"7","author":"A.Amolik","year":"2016","journal-title":"IACSIT International Journal of Engineering and Technology"},{"key":"IJDSST.2019070101-1","first-page":"339","article-title":"Aspect Extraction through Semi-Supervise Modeling.","year":"2012","journal-title":"50th Annual Meeting of the Association for Computational Linguistics (ACL\u201912)"},{"issue":"1","key":"IJDSST.2019070101-2","first-page":"82","article-title":"Sentiment Analysis Methodology of Twitter Data with an application on Hajj season.","volume":"2","author":"M.Elgamal","year":"2016","journal-title":"International Journal of Engineering Research & Science"},{"key":"IJDSST.2019070101-3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2006.00277.x"},{"issue":"3","key":"IJDSST.2019070101-4","first-page":"4782","article-title":"Content based Recommender System on Customer Reviews using Sentiment Classification Algorithms.","volume":"5","author":"R.Keshav","year":"2014","journal-title":"International Journal of Computer Science and Information Technology"},{"issue":"2","key":"IJDSST.2019070101-5","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5958\/2249-3220.2015.00013.0","article-title":"Investigating Issues and Challenges of Sentiment Analysis Based Recommender System.","volume":"5","author":"V. 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In ACL \u201902 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 7-12 (pp. 417-424).","DOI":"10.3115\/1073083.1073153"},{"key":"IJDSST.2019070101-9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3115\/v1\/W14-5905","article-title":"A Rule-Based Approach to Aspect Extraction from Product Reviews.","author":"S.Poria","year":"2014","journal-title":"The Second Workshop on Natural Language Processing for Social Media (SocialNLP)"},{"issue":"9","key":"IJDSST.2019070101-10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.5120\/ijca2016907977","article-title":"A survey on Sentiment Analysis Algorithms for opinion mining.","volume":"133","author":"V. M.Pradhan","year":"2016","journal-title":"International Journal of Computers and Applications"},{"key":"IJDSST.2019070101-11","unstructured":"Qiming, D., Minghui, Q., Chao-Yuan, W. J. S. A., Jing, J., & Chong, A. W. (2014). 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