Building Dynamic Lexicons for Sentiment Analysis
Open Access
- 17 May 2019
- journal article
- research article
- Published by IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial in INTELIGENCIA ARTIFICIAL
- Vol. 22 (64), 1-13
- https://doi.org/10.4114/intartif.vol22iss64pp1-13
Abstract
Nowadays, many approaches for Sentiment Analysis (SA) rely on affective lexicons to identify emotions transmitted in opinions. However, most of these lexicons do not consider that a word can express different sentiments in different predication domains, introducing errors in the sentiment inference. Due to this problem, we 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 results of a simple rule-based sentiment classier using the corresponding DL, with the results obtained using a general affective lexicon. For most corpora containing specic domain opinions, the DL reaches better results than the general lexicon.Keywords
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