{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:16:04Z","timestamp":1780420564511,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,17]],"date-time":"2017-11-17T00:00:00Z","timestamp":1510876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In the latest studies concerning the sentiment polarity of words, the authors mostly consider the positive and negative constructions, without paying too much attention to the neutral words, which can have, in fact, significant sentiment degrees. More precisely, not all the neutral words have zero positivity or negativity scores, some of them having quite important nonzero scores for these polarities. At this moment, in the literature, a word is considered neutral if its positive and negative scores are equal, which implies two possibilities: (1) zero positive and negative scores; (2) nonzero, but equal positive and negative scores. It is obvious that these cases represent two different categories of neutral words that must be treated separately by a sentiment analysis task. In this paper, we present a comprehensive study about the neutral words applied to English as is developed with the aid of SentiWordNet 3.0: the publicly available lexical resource for opinion mining. We designed our study in order to provide an accurate classification of the so-called \u201cneutral words\u201d described in terms of sentiment scores and using measures from neutrosophy theory. The intended scope is to fill the gap concerning the neutrality aspect by giving precise measurements for the words\u2019 objectivity.<\/jats:p>","DOI":"10.3390\/sym9110280","type":"journal-article","created":{"date-parts":[[2017,11,17]],"date-time":"2017-11-17T12:00:20Z","timestamp":1510920020000},"page":"280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5332-2220","authenticated-orcid":false,"given":"Mihaela","family":"Colhon","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Sciences, University of Craiova, Craiova 200585, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u015etefan","family":"Vl\u0103du\u0163escu","sequence":"additional","affiliation":[{"name":"Faculty of Letters, University of Craiova, Craiova 200585, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xenia","family":"Negrea","sequence":"additional","affiliation":[{"name":"Faculty of Letters, University of Craiova, Craiova 200585, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Representation of a Sentence using a Polar Fuzzy Neutrosophic Semantic Net","volume":"4","author":"Lakra","year":"2014","journal-title":"Int. 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