Inteligencia Artificial <p style="text-align: justify;"><span style="color: #000000;"><strong><em><a style="color: #003366; text-decoration: underline;" href="" target="_blank" rel="noopener">Inteligencia Artificial</a></em></strong><span id="result_box" class="" lang="en"> is an international open access journal promoted by <span class="">the Iberoamerican Society of</span> Artificial Intelligence (<a href="">IBERAMIA</a>), following the <em>Open Journal System (<span style="color: #050e69;"><a style="color: #050e69;" href="">OJS</a></span>)</em> standard.</span></span></p> <p style="text-align: justify;">The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Articles can <span class="">be written</span> in Spanish, <span class="">Portuguese and English,</span> and they will undergo a peer revision process. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs and special issues on topics of special relevance to the AI community.</p> en-US <p>Open Access publishing.<br>Lic. under Creative Commons CC-BY-NC<br>Inteligencia Artificial (Ed. IBERAMIA)<br>ISSN: 1988-3064 (on line).<br>(C) IBERAMIA &amp; The Authors</p> (editor) (Technical Contact. Webmaster (Only technical issues)) Mon, 01 Jul 2019 10:38:27 +0200 OJS 60 Building Dynamic Lexicons for Sentiment Analysis <p>Nowadays, many approaches for Sentiment Analysis (SA) rely on affective lexicons to identify emotions&nbsp;transmitted in opinions. However, most of these lexicons do not consider that a word can express different&nbsp;sentiments in different predication domains, introducing errors in the sentiment inference. Due to this problem,&nbsp;we present a model based on a context-graph which can be used for building domain specic sentiment lexicons<br>(DL: Dynamic Lexicons) by propagating the valence of a few seed words. For different corpora, we compare the&nbsp;results of a simple rule-based sentiment classier using the corresponding DL, with the results obtained using a&nbsp;general affective lexicon. For most corpora containing specic domain opinions, the DL reaches better results&nbsp;than the general lexicon.<br><br></p> Nicolás Mechulam, Damián Salvia, Aiala Rosá, Mathias Etcheverry ##submission.copyrightStatement## Fri, 17 May 2019 00:00:00 +0200 Feature Learning with Multi-objective Evolutionary Computation in the generation of Acoustic Features <p>To choice audio features has been a very interesting theme for audio classification experts. They have seen that this process is probably the most important effort to solve the classification problem. In this sense, there are techniques of <em>Feature Learning</em>&nbsp;for generate new features more suitable for classification model than conventional features. However, these techniques generally do not depend on knowledge domain and they can apply in various types of raw data. However, less agnostic approaches&nbsp;learn a type of knowledge restricted to the area studded. The audio data requires a specific knowledge type. There are many techniques that seek to improve the performance of the new generation of acoustic features, among which stands the technique that use evolutionary algorithms to explore analytical space of function. However, the efforts made leave opportunities for improvement. The purpose of this work is to propose and evaluate a multi-objective alternative to the exploitation of analytical audio features. In addition, experiments were arranged to be validated the method, with the help a computational prototype that implemented the proposed solution. After it was found the effectiveness of the model and ensuring that there is still opportunity for improvement in the chosen segment.</p> José Antonio Alves Menezes, Giordano Cabral, Bruno Gomes, Paulo Pereira ##submission.copyrightStatement## Mon, 01 Jul 2019 10:37:58 +0200