A systemic and cybernetic perspective on causality, big data and social networks in tourism

Purpose The purpose of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on an algorithm and applying an interpretation of chaos theory developed in the context of General Systems Theory and Big Data. Design/methodology/approach Tourism is one of the most digitalized sectors of the economy, and social networks are an important source of data for information gathering.

Overview of TASS 2018: Opinions, Health and Emotions Resumen de TASS 2018: Opiniones, Salud y Emociones

This is an overview of the Workshop on Semantic Analysis at the SEPLN congress held in Sevilla, Spain, in September 2018. This forum proposes to participants four different semantic tasks on texts written in Spanish. Task 1 focuses on polarity classification; Task 2 encourages the development of aspect-based polarity classification systems; Task 3 provides a scenario for discovering knowledge from eHealth documents; finally, Task 4 is about automatic classification of news articles according to safety. The former two tasks are novel in this TASS’s edition.

TASS-2018-Task 3. eHealth Knowledge Discovery

Natural Language Processing (NLP) methods are increasingly being used to mine knowledge from unstructured health texts. Recent advances in health text processing techniques are encouraging researchers and medical domain experts to go beyond just reading the information included in published texts (e.g. academic manuscripts, clinical reports, etc.) and structured questionnaires, to discover new knowledge by mining health contents. This has allowed other perspectives to surface that were not previously available.

A Bootstrapping Technique to Annotate Emotional Corpora Automatically

In computational linguistics, the increasing interest of the detection of emotional and personality profiles has given birth to the creation of resources that allow the detection of these profiles. This is due to the large number of applications that the detection of emotion states can have, such as in e-learning environment or suicide prevention. The development of resources for emotional profiles can help to improve emotion detection techniques such as supervised machine learning, where the development of annotated corpora is crucial.

Exploiting a Bootstrapping Approach for Automatic Annotation of Emotions in Texts

The objective of this research is to develop a technique to automatically annotate emotional corpora. The complexity of automatic annotation of emotional corpora still presents numerous challenges and thus there is a need to develop a technique that allow us to tackle the annotation task. The relevance of this research is demonstrated by the fact that people's emotions and the patterns of these emotions provide a great value for business, individuals, society or politics. Hence, the creation of a robust emotion detection system becomes crucial.

DrugSemantics: A corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics

For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care. The problem is that such information is stored in different sources and their consultation time is limited.

Convenio de colaboración entre el GPLSI e INVAT.TUR

Jueves, 1 Diciembre, 2016

El GPLSI ha establecido un convenio de colaboración con el Instituto Valenciano de Tecnologías Turísticas (Invat.tur) en el que  se ofrecen servicios informáticos de analítica social a través de la plataforma GPLSI Social Analytics. 

GPLSI Sentiment Analysis V1.0: Análisis de sentimientos en textos

Resumen de la aplicación: GPLSI Sentiment Analysis v1.0 constituye un servicio web con tecnología RESTful que a su vez involucra librerías de programación útiles para sistemas de terceros. El objetivo de esta tecnología es analizar textos en idioma Inglés y detectar la polaridad de los sentimientos (estados de opinión) expresados. La funcionalidad para detectar la polaridad de los sentimientos implica dos niveles de granularidad: polaridad a nivel global (PNG) y polaridad a nivel de aspectos (PNA).

GPLSI Emotion Analysis V1.0: Análisis de emociones en textos

Resumen de la aplicación: GPLSI Emotion Analysis v1.0 constituye un servicio web con tecnología RESTful que a su vez involucra librerías de programación útiles para sistemas de terceros. El objetivo de esta tecnología es analizar textos en idioma Inglés para detectar y clasificar las emociones expresadas. La Detección y Clasificación de Emociones en textos permite identificar y clasificar las emociones expresadas en un texto. La herramienta aquí descrita es capaz de discriminar entre el siguiente conjunto de emociones: alegría, tristeza, disgusto, miedo y sorpresa.

Clase de la obra: 
Programa de ordenador


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