{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:53:09Z","timestamp":1780073589602,"version":"3.54.0"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T00:00:00Z","timestamp":1551830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.<\/jats:p>","DOI":"10.3390\/info10030098","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T10:52:22Z","timestamp":1551955942000},"page":"98","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Detecting Emotions in English and Arabic Tweets"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2600-5021","authenticated-orcid":false,"given":"Tariq","family":"Ahmad","sequence":"first","affiliation":[{"name":"School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9903-0722","authenticated-orcid":false,"given":"Allan","family":"Ramsay","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9313-5849","authenticated-orcid":false,"given":"Hanady","family":"Ahmed","sequence":"additional","affiliation":[{"name":"CAS, Arabic Department, Qatar University, Al Hala St, P.O. Box 2713 Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ijpsycho.2012.08.012","article-title":"Heart rate variability is associated with emotion recognition: Direct evidence for a relationship between the autonomic nervous system and social cognition","volume":"86","author":"Quintana","year":"2012","journal-title":"Int. J. Psychophysiol."},{"key":"ref_2","unstructured":"Nakasone, A., Prendinger, H., and Ishizuka, M. (2005, January 6\u20138). Emotion recognition from electromyography and skin conductance. Proceedings of the 5th International Workshop on Biosignal Interpretation, Tokyo, Japan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C.M., Kazemzadeh, A., Lee, S., Neumann, U., and Narayanan, S. (2004, January 13\u201315). 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