El pasado 29 de abril se celebró la entrega de la primera edición de las becas GSI, presidida por el Rector Guillermo Cisneros. Durante el acto, se inauguró la placa del Laboratorio de Sistemas Inteligentes Profesores Gregorio Fernández, Mercedes Garijo y Fernando Sáez Vacas.
Más información del acto en fotografías: https://www.flickr.com/photos/etsit-upm/sets/72157691193114953/with/40774042453/
El grupo de Sistemas Inteligentes organiza un acto de entrega de la Primera Edición de las Becas de Iniciación a la investigación en Sistemas Inteligentes:
El artículo A framework for fake review detection in online consumer electronics retailers, por Rodrigo Barbado, Oscar Araque y Carlos A. Iglesias has sido aceptado y publicado en la revista Information Processing & Management. Esta revista se encuentra indexada en JCR: (Q1, 3.444).
Abstract:The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82% F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test.
Referencia:
Oscar Araque, Ganggao Zhu, Carlos A. Iglesias, A semantic similarity-based perspective of affect lexicons for sentiment analysis, Knowledge-Based Systems, Volume 165, 2019, Pages 346-359, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2018.12.005. (http://www.sciencedirect.com/science/article/pii/S0950705118305926).
Este lunes 11 de febrero de 2019 se ha celebrado el acto de entrega de premios en el Paraninfo de la Universidad Politécnica de Madrid de la III convocatoria de premios de la Cátedra Ingeniero General. D. Antonio Remón y Zarco del Valle.
Felicidades a Tasio Méndez que ha recibido el primer premio A la mejor tecnología, producto o servicio desarrollada en la UPM con temática relacionada con Defensa y Seguridad por su Trabajo de Fin de Grado titulado "Design and implementation of a visualization module for agent-based social simulations applied to radicalism spread".
El artículo A semantic similarity-based perspective of affect lexicons for sentiment analysis, por Oscar Araque, Ganggao Zhu y Carlos A. Iglesias has sido aceptado y publicado en la revista Knowledge-Based Systems. Esta revista se encuentra indexada en JCR: (Q1, 4.396).
Referencia:
Oscar Araque, Ganggao Zhu, Carlos A. Iglesias, A semantic similarity-based perspective of affect lexicons for sentiment analysis, Knowledge-Based Systems, Volume 165, 2019, Pages 346-359, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2018.12.005. (http://www.sciencedirect.com/science/article/pii/S0950705118305926).
Abstract: Lexical resources are widely popular in the field of Sentiment Analysis, as they represent a resource that directly encodes sentimental knowledge. Usually sentiment lexica are used for polarity estimation through the matching of words contained in a text and their associated lexicon sentiment polarities. Nevertheless, such resources have limitations in vocabulary coverage and domain adaptation. Besides, many recent techniques exploit the concept of distributed semantics, normally through word embeddings. In this work, a semantic similarity metric is computed between text words and lexica vocabulary. Using this metric, this paper proposes a sentiment classification model that uses the semantic similarity measure in combination with embedding representations. In order to assess the effectiveness of this model, we perform an extensive evaluation. Experiments show that the proposed method can improve Sentiment Analysis performance over a strong baseline, being this improvement statistically significant. Finally, some characteristics of the proposed technique are studied, showing that the selection of lexicon words has an effect in cross-dataset performance.
Keywords:
Sentiment analysis; Sentiment lexicon; Semantic similarity; Word embeddings
