{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:14:39Z","timestamp":1762341279861},"reference-count":0,"publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","issue":"64","license":[{"start":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T00:00:00Z","timestamp":1558051200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ia"],"abstract":"<jats:p>Nowadays, many approaches for Sentiment Analysis (SA) rely on affective lexicons to identify emotions\u00a0transmitted in opinions. However, most of these lexicons do not consider that a word can express different\u00a0sentiments in different predication domains, introducing errors in the sentiment inference. Due to this problem,\u00a0we present a model based on a context-graph which can be used for building domain specic sentiment lexicons(DL: Dynamic Lexicons) by propagating the valence of a few seed words. For different corpora, we compare the\u00a0results of a simple rule-based sentiment classier using the corresponding DL, with the results obtained using a\u00a0general affective lexicon. For most corpora containing specic domain opinions, the DL reaches better results\u00a0than the general lexicon.<\/jats:p>","DOI":"10.4114\/intartif.vol22iss64pp1-13","type":"journal-article","created":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T03:13:01Z","timestamp":1562037181000},"page":"1-13","source":"Crossref","is-referenced-by-count":1,"title":["Building Dynamic Lexicons for Sentiment Analysis"],"prefix":"10.4114","volume":"22","author":[{"given":"Nicol\u00e1s","family":"Mechulam","sequence":"first","affiliation":[]},{"given":"Dami\u00e1n","family":"Salvia","sequence":"first","affiliation":[]},{"given":"Aiala","family":"Ros\u00e1","sequence":"first","affiliation":[]},{"given":"Mathias","family":"Etcheverry","sequence":"first","affiliation":[]}],"member":"2598","published-online":{"date-parts":[[2019,5,17]]},"container-title":["Inteligencia Artificial"],"original-title":[],"link":[{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/244\/90","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/244\/90","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T03:13:03Z","timestamp":1562037183000},"score":1,"resource":{"primary":{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/view\/244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,17]]},"references-count":0,"journal-issue":{"issue":"64","published-online":{"date-parts":[[2019,7,1]]}},"URL":"https:\/\/doi.org\/10.4114\/intartif.vol22iss64pp1-13","relation":{},"ISSN":["1988-3064","1137-3601"],"issn-type":[{"value":"1988-3064","type":"electronic"},{"value":"1137-3601","type":"print"}],"subject":[],"published":{"date-parts":[[2019,5,17]]}}}