Adapting data-driven research to the fields of social sciences and the humanities

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline

Sentiment Analysis for Fake News Detection

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2020/11This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years

Identificação da valência emocional em sentenças de contos infantis

Orientador: Paula Dornhofer Paro CostaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A análise de sentimentos em textos tem sido amplamente explorada recentemente, principalmente usando técnicas de processamento de linguagem natural e aprendizado de máquina. No entanto, apesar dos avanços alcançados, ainda existem desafios significativos. Nosso trabalho explora a análise de sentimentos em textos narrativos, identificando as valências emocionais em sentenças pertencentes a contos infantis, que podem ser usadas, por exemplo, como recurso para aplicações destinadas a sintetizar narradores e atores virtuais no idioma português do Brasil. Usando técnicas de processamento de linguagem natural e um banco de dados afetivo chamado Anew-Br, criamos nosso algoritmo EMONT V1, que atribui valência emocional às frases do corpus desenvolvido. Foram adotadas duas abordagens diferentes para obter resultados comparáveis, aumentando assim a confiabilidade do nosso sistema: uma avaliação subjetiva que visa rotular frases por um grupo de 100 voluntários, que assumimos ser grund truth, e uma avaliação objetiva comparando os rótulos fornecidos por algumas plataformas comerciais que prometem fornecer funcionalidades semelhantes. Nosso algoritmo alcançou um desempenho de precisão equivalente a setores importantes de serviços de análise de sentimentos, como IBM Watson, API do Google Cloud Natural Language e Microsoft Azure Text Analytics. Os resultados dessa metodologia podem ser estendidos para outras frases infantis ou textos semelhantes, por exemplo, romance, história curta, crônica, fábula, parábola, anedota ou lendaAbstract: Sentiment analysis in texts has been widely explored recently, mainly using natural language processing and machine learning techniques. However, despite the advances achieved, there are still significant challenges. Our work explores the analysis of sentiments in narrative texts by identifying the emotional valences in sentences belonging to children's tales, which can be used, for example, as a resource for applications aimed at synthesizing narrators and virtual actors in the Brazilian Portuguese language. Using Natural Language Processing techniques and an affective database called Anew-Br, we created our EMONT V1 algorithm, which attributes emotional valence to the phrases of the developed corpus. Two different approaches were taken to obtain comparable results, thereby increasing the reliability of our system: a subjective assessment that aims to label sentences by a group of 100 volunteers, which we assume to be grund truth, and an objective assessment comparing the labels provided by some commercial platforms that promise to provide similar functionality. Our algorithm has achieved precision performance equivalent to significant industries of sentiment analysis services, such as IBM Watson, Google Cloud Natural Language API, and Microsoft Azure Text Analytics. The results of this methodology can be extended to other children sentences or similar texts, for instance, romance, short story, chronicle, fable, parable, anecdote, or legendMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica149147/2016-3CNP

Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment