Character N-Grams for Detecting Deceptive Controversial Opinions

[EN] Controversial topics are present in the everyday life, and opinions about them can be either truthful or deceptive. Deceptive opinions are emitted to mislead other people in order to gain some advantage. In the most of the cases humans cannot detect whether the opinion is deceptive or truthful, however, computational approaches have been used successfully for this purpose. In this work, we evaluate a representation based on character n-grams features for detecting deceptive opinions. We consider opinions on the following: abortion, death penalty and personal feelings about the best friend; three domains studied in the state of the art. We found character n-grams effective for detecting deception in these controversial domains, even more than using psycholinguistic features. Our results indicate that this representation is able to capture relevant information about style and content useful for this task. This fact allows us to conclude that the proposed one is a competitive text representation with a good trade-off between simplicity and performance.We would like to thank CONACyT for partially supporting this work under grants 613411, CB-2015-01-257383, and FC-2016/2410. The work of the last author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).Sánchez-Junquera, JJ.; Luis Villaseñor Pineda; Montes Gomez, M.; Rosso, P. (2018). Character N-Grams for Detecting Deceptive Controversial Opinions. Lecture Notes in Computer Science. 11018:135-140. https://doi.org/10.1007/978-3-319-98932-7_13S13514011018Aritsugi, M., et al.: Combining word and character n-grams for detecting deceptive opinions, vol. 1, pp. 828–833. IEEE (2017)Buller, D.B., Burgoon, J.K.: Interpersonal deception theory. Commun. Theory 6(3), 203–242 (1996)Cagnina, L.C., Rosso, P.: Detecting deceptive opinions: intra and cross-domain classification using an efficient representation. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25(Suppl. 2), 151–174 (2017)Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection, pp. 171–175. Association for Computational Linguistics (2012)Fusilier, D.H., Montes-y-Gómez, M., Rosso, P., Cabrera, R.G.: Detection of opinion spam with character n-grams. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 285–294. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_21Hernández-Castañeda, Á., Calvo, H., Gelbukh, A., Flores, J.J.G.: Cross-domain deception detection using support vector networks. Soft Comput. 21(3), 1–11 (2016)Mihalcea, R., Strapparava, C.: The lie detector: explorations in the automatic recognition of deceptive language. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 309–312. Association for Computational Linguistics (2009)Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 309–319. Association for Computational Linguistics (2011)Pérez-Rosas, V., Mihalcea, R.: Cross-cultural deception detection. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 440–445 (2014)Sapkota, U., Solorio, T., Montes-y-Gómez, M., Bethard, S.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)Vrij, A.: Detecting Lies and Deceit: Pitfalls and Opportunities. Wiley, Hoboken (2008

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Líneas de investigación en computer imagery

En la actualidad los gráficos se han convertido en uno de los medios de comunicación más naturales que existen. Esto se debe a la habilidad inherente de las personas de reconocer patrones en 2D y 3D que les permite percibir y procesar información de datos gráficos en forma rápida y eficiente. Paralelamente el uso de las computadoras ha crecido de manera que permite la creación, almacenamiento y manipulación de modelos e imágenes de objetos. Estos modelos provienen de una diversidad de campos tales como la física, matemática, ingeniería, arquitectura, fenómenos naturales, etc. En este contexto, las imágenes son potenciales herramientas para la toma de decisiones dado que permiten aumentar la información transmitida. Sin embargo, el crear y reproducir imágenes presenta problemas específicos a la manera en que estas pretenden utilizarse. El área de los gráficos por computadora (computer imagery) puede dividirse en tres grandes campos que interactúan entre sí: la computación gráfica, el procesamiento de imágenes y la visión por computadora. La computación gráfica se ocupa de la síntesis gráfica de objetos reales e imaginarios obtenidos a partir de modelos generados computacionalmente. El procesamiento de imágenes trata el análisis y manipulación de imágenes ya existentes; donde la nueva imagen generada es de alguna manera diferente a la imagen original. En particular, el análisis de imágenes es importante para áreas tales como la biomedicina, imágenes de reconocimiento aéreo, scan de cromosomas, etc. Esta rama posee sub-areas tales como: realce (enhancement) de imágenes, detección y reconocimiento de patrones, análisis de escenas, etc. Por último, el campo de visión por computadora se relaciona con la extracción de información a partir de una imagen (imágenes capturadas desde el 'ojo' de robots) para la reconstrucción de escenas en 3D a partir de modelos de 2D, intentando emular el sistema visual humano.Eje: Visualización - Computación GráficaRed de Universidades con Carreras en Informática (RedUNCI

Líneas de investigación en computer imagery

En la actualidad los gráficos se han convertido en uno de los medios de comunicación más naturales que existen. Esto se debe a la habilidad inherente de las personas de reconocer patrones en 2D y 3D que les permite percibir y procesar información de datos gráficos en forma rápida y eficiente. Paralelamente el uso de las computadoras ha crecido de manera que permite la creación, almacenamiento y manipulación de modelos e imágenes de objetos. Estos modelos provienen de una diversidad de campos tales como la física, matemática, ingeniería, arquitectura, fenómenos naturales, etc. En este contexto, las imágenes son potenciales herramientas para la toma de decisiones dado que permiten aumentar la información transmitida. Sin embargo, el crear y reproducir imágenes presenta problemas específicos a la manera en que estas pretenden utilizarse. El área de los gráficos por computadora (computer imagery) puede dividirse en tres grandes campos que interactúan entre sí: la computación gráfica, el procesamiento de imágenes y la visión por computadora. La computación gráfica se ocupa de la síntesis gráfica de objetos reales e imaginarios obtenidos a partir de modelos generados computacionalmente. El procesamiento de imágenes trata el análisis y manipulación de imágenes ya existentes; donde la nueva imagen generada es de alguna manera diferente a la imagen original. En particular, el análisis de imágenes es importante para áreas tales como la biomedicina, imágenes de reconocimiento aéreo, scan de cromosomas, etc. Esta rama posee sub-areas tales como: realce (enhancement) de imágenes, detección y reconocimiento de patrones, análisis de escenas, etc. Por último, el campo de visión por computadora se relaciona con la extracción de información a partir de una imagen (imágenes capturadas desde el 'ojo' de robots) para la reconstrucción de escenas en 3D a partir de modelos de 2D, intentando emular el sistema visual humano.Eje: Visualización - Computación GráficaRed de Universidades con Carreras en Informática (RedUNCI

Head-to-head comparison of two angiography-derived fractional flow reserve techniques in patients with high-risk acute coronary syndrome: A multicenter prospective study.

FFRangio and QFR are angiography-based technologies that have been validated in patients with stable coronary artery disease. No head-to-head comparison to invasive fractional flow reserve (FFR) has been reported to date in patients with acute coronary syndromes (ACS). This study is a subset of a larger prospective multicenter, single-arm study that involved patients diagnosed with high-risk ACS in whom 30-70% stenosis was evaluated by FFR. FFRangio and QFR - both calculated offline by 2 different and blinded operators - were calculated and compared to FFR. The two co-primary endpoints were the comparison of the Pearson correlation coefficient between FFRangio and QFR with FFR and the comparison of their inter-observer variability. Among 134 high-risk ACS screened patients, 59 patients with 84 vessels underwent FFR measurements and were included in this study. The mean FFR value was 0.82 ± 0.40 with 32 (38%) being ≤0.80. The mean FFRangio was 0.82 ± 0.20 and the mean QFR was 0.82 ± 0.30, with 27 (32%) and 25 (29%) being ≤0.80, respectively. The Pearson correlation coefficient was significantly better for FFRangio compared to QFR, with R values of 0.76 and 0.61, respectively (p = 0.01). The inter-observer agreement was also significantly better for FFRangio compared to QFR (0.86 vs 0.79, p < 0.05). FFRangio had 91% sensitivity, 100% specificity, and 96.8% accuracy, while QFR exhibited 86.4% sensitivity, 98.4% specificity, and 93.7% accuracy. In patients with high-risk ACS, FFRangio and QFR demonstrated excellent diagnostic performance. FFRangio seems to have better correlation to invasive FFR compared to QFR but further larger validation studies are required

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

GZMKhigh CD8+ T effector memory cells are associated with CD15high neutrophil abundance in non-metastatic colorectal tumors and predict poor clinical outcome.

CD8(+) T cells are a major prognostic determinant in solid tumors, including colorectal cancer (CRC). However, understanding how the interplay between different immune cells impacts on clinical outcome is still in its infancy. Here, we describe that the interaction of tumor infiltrating neutrophils expressing high levels of CD15 with CD8(+) T effector memory cells (T(EM)) correlates with tumor progression. Mechanistically, stromal cell-derived factor-1 (CXCL12/SDF-1) promotes the retention of neutrophils within tumors, increasing the crosstalk with CD8(+) T cells. As a consequence of the contact-mediated interaction with neutrophils, CD8(+) T cells are skewed to produce high levels of GZMK, which in turn decreases E-cadherin on the intestinal epithelium and favors tumor progression. Overall, our results highlight the emergence of GZMK(high) CD8(+) T(EM) in non-metastatic CRC tumors as a hallmark driven by the interaction with neutrophils, which could implement current patient stratification and be targeted by novel therapeutics

Internet e vino. Dialoghi, informazioni e scelte nei processi di acquisto e nei sistemi di offerta vitivinicola

Nel capitolo sono affrontate le tematiche della relazione tra nuove tecnologie dell'informazione (in particolare internet)e i soggetti della costellazione del valore vitivinicolo. Tale relazione viene articolata attraverso l'analisi delle sue implicazioni dal punto di vista dei processi di acquisto della domanda e da quello strategico-operativo dei soggetti dell'offerta