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First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen\u2019s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Na\u00efve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Na\u00efve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.<\/jats:p>","DOI":"10.1177\/0165551520930917","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T00:57:36Z","timestamp":1592269056000},"page":"712-726","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":30,"title":["Sentiment analysis of tweets through Altmetrics: A machine learning approach"],"prefix":"10.1177","volume":"47","author":[{"given":"Saeed-Ul","family":"Hassan","sequence":"first","affiliation":[{"name":"Information Technology University, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aneela","family":"Saleem","sequence":"additional","affiliation":[{"name":"Information Technology University, 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