ResumenEste artículo presenta un método para la creación de diccionarios marcados con un valor específico (por ejemplo, las emociones, la polaridad) para su uso en varias tareas de procesamiento de lenguaje natural realizadas por computadoras. En el diccionario creado las palabras seleccionadas se han etiquetado con seis emociones básicas. Para eso, las palabras primero fueron analizadas (anotadas) manualmente por múltiples evaluadores, y ponderadas automáticamente a base de éstas. El método se aplicó para el idioma español.Las palabras que conforman al diccionario fueron etiquetadas con las categorías emocionales básicas: alegría, enojo, miedo, tristeza, sorpresa y repulsión. A diferencia de otros diccionarios para computadoras, el diccionario propuesto contiene ponderaciones porcentajes de probabilidad de ser usadas con un sentido emocional. Cada palabra fue valorada por múltiples evaluadores, y posteriormente se realizó un análisis de concordancia con el método de kappa ponderado, adaptándolo para evaluadores múltiples. Con los resultados obtenidos, se propuso una medida que estima que tan frecuente es el uso afectivo de una palabra: factor de probabilidad de uso afectivo (FPA), el cual sirve para dotar a las palabras potencialmente emocionales con un factor de peso. El FPA puede ser incluido como información en sistemas automáticos, por ejemplo, para detección de sentimientos en texto. El FPA se refiere a la tendencia del uso de cada palabra, no es una característica absoluta. Así es útil para los sistemas automáticos.Palabras clave: diccionario marcado con emociones, factor de probabilidad de uso afectivo, concordancia entre evaluadores, análisis de sentimientos, método de kappa ponderado. Onomazein AbstractThis paper presents a method for creation of dictionaries marked with specific values (for example, emotions, polarity) for use in various tasks of automatic natural language processing. In the created dictionary, the selected words are tagged with six basic emotions.For this, they are first analyzed (annotated) manually by multiple annotators and automatically weighted on the basis of these evaluations. The method was applied to the Spanish language. The paradigm chosen for tagging of the words that form the dictionary corresponds to basic emotional categories: joy, anger, fear, sadness, surprise and disgust.Unlike other dictionaries, our dictionary contains weights that correspond to percentages of probability of being used with the sense related to emotion. Each word was evaluated by multiple annotators, and subsequently the agreement between them was analyzed with the method of weighted kappa adapted for multiple entries. On the basis of these results, we propose a new measure that estimates the probability of the affective use: probability factor of affective use (PFA), which serves to provide the potentially emotional words with the weight. PFA can be used as data in automatic systems for emotional analysis of texts. PFA refers to the tendency of the use of each word, which is useful for automatic ...

This paper presents an original work for aircraft noise monitoring systems and it analyzes the airplanes noise signals and a method to identify them. The method uses processed spectral patterns and a neuronal network feed-forward, programmed by means of virtual instruments. The obtained results, very useful in portable systems, make possible to introduce redundancy to permanent monitoring systems. The noise level in a city has fluctuations between 50 dB (A) and 100 dB (A). It depends on the population density and its activity, commerce and services in the public thoroughfare, terrestrial and aerial urban traffic, of the typical activities of labor facilities and used machinery, which give varied conditions that must be faced of diverse ways within the corresponding normalization. The sounds or noises that exceed the permissible limits, whichever the activities or causes that originate them, are considered events susceptible to degrade the environment and the health.

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Abstract. This paper presents a novel computational multimodal model designed for pattern recognition of aircrafts' noise in real environments; with an 88.5% of effectiveness, it considers 13 different categories of aircrafts. This method includes measurements of signals of the noise produced during the takeoff at 25,000 samples per second and with a resolution of 24 bits, an spectral analysis made by means of an autoregressive model, an octave analysis, a normalization method created specifically for this work and two feed-forward neural networks. All the signals used for the design and evaluation of the results were obtained by means of field measurements.