Sentiment analysis is a relevant area in the natural language processing context–(NLP) that allows extracting opinions about different topics such as customer service and political elections. Sentiment analysis is usually carried out through supervised learning approaches and using labeled data. However, obtaining such labels is generally expensive or even infeasible. The above problems can be faced by using models based on self-supervised learning, which aims to deal with various machine learning paradigms in the absence of labels. Accordingly, we propose a self-supervised approach for sentiment analysis in Spanish that comprises a lexicon-based method and a supervised classifier. We test our proposal over three corpora; the first two are labeled datasets, namely, CorpusCine and PaperReviews. Further, we use an unlabeled corpus conformed by news related to the Colombian conflict to understand the university journalistic narrative of the war in Colombia. Obtained results demonstrate that our proposal can deal with sentiment analysis settings in scenarios with unlabeled corpus; in fact, it acquires competitive performance compared with state-of-the-art techniques in partially-labeled datasets.
This article is concerned with narrating the research-creation experience that emerged from the CAPAZ project (Analytical Center for University Productions in the Framework of Conflict). It aims to account for the emergence of the transmedia web platform “RUTAS: University Peace Stories in Colombia,” based on the analysis of the media representations produced by the national opinion sector of university journalism that are concerned with peace narratives from 2000 to 2021. A mixed methodological approach and an experimental scheme with Big Data techniques derived from an automated lexicometric analysis led to the implementation of a strategy for creating pedagogical narratives that categorized the results obtained. Thus, RUTAS was born as an exercise involving the deployment of knowledge and creative skills around the notions of design as a possibility and hypermediality as a scenario of digital and interactive stories, housed in virtual, cultural, and open platforms that would give rise to multiple representations and media appropriations in the field of communication and education.
Este capítulo presenta la ruta narrativa pedagógica, que, en el marco de RUTAS, el primer proyecto de CAPAZ, Centro analítico de producciones universitarias en el marco del conflicto, se desarrolló para evidenciar la estructura de las representaciones mediáticas del periodismo universitario acerca del proceso de paz, la memoria de las víctimas y el conflicto armado colombiano en los periodos 2000 y 2021. El ejercicio que aquí se documenta tiene lugar en una segunda fase metodológica del proyecto. Allí los datos provenientes del análisis estadístico textual obtenidos bajo el método Alceste-Reinert (2003) posibilitaron la visualización de mundos lexicales en cartografías, dendrogramas y análisis factoriales de correspondencia.En esta fase se evidencia la intención en los relatos periodísticos a partir de un análisis que ubica relaciones de inferencia entre clústeres, territorios, medios y soportes, planteando además una relación de estos con actantes (Greimas, 1987), posturas y ejes de la comunicación. De allí surgen las reflexiones y síntesis que marcan la narratividad del proyecto.
El presente libro destaca el papel de la pedagogía y al desarrollo humano; el proceso de enseñanza-aprendizaje como eje de discusión de la investigación en educación; comunicación organizacional: gestión interna, proyección y auditoría y comunicación; y culturas mediáticas: una aproximación a las coordenadas del sentido
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