Júlio Cesar dos Reis

An evaluation of machine translation for multilingual sentence-level sentiment analysis

Sentiment analysis has become a key tool for several social media applications, including analysis of users opinions about products and services, support to politics during campaigns and even for market trending. There are multiple existing sentiment analysis methods that explore different techniques, usually relying on lexical resources or learning approaches. Despite the large interest on this theme and amount of research efforts in the field, almost all existing methods are designed to work with only English content. Most existing strategies in specific languages consist of adapting existing lexical resources, without presenting proper validations and basic baseline comparisons. In this paper, we take a different step into this field. We focus on evaluating existing efforts proposed to do language specific sentiment analysis. To do it, we evaluated twenty-one methods for sentence-level sentiment analysis proposed for English, comparing them with two language-specific methods. Based on nine language-specific datasets, we provide an extensive quantitative analysis of existing multi-language approaches. Our main result suggests that simply translating the input text on a specific language to English and then using one of the existing English methods can be better than the existing language specific efforts evaluated. We also rank those implementations comparing their prediction performance and identifying the methods that acquired the best results using machine translation across different languages. As a final contribution to the research community, we release our codes and datasets. We hope our effort can help sentiment analysis to become English independent.

Demographics of News Sharing in the U.S. Twittersphere

The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to be more active in terms of sharing news, biasing the news audience to the interests of these demographic groups. Our results also quantify differences in interests of news sharing across demographics, which has implications for personalized news digests.

Desempenho Macroeconômico e Taxa de Pobreza Estadual no Brasil no Período 1981-2006

As discussoes sobre a pobreza sempre estiveram presentes no âmbito da teoria economica. Contudo, sao relativamente escassos, ainda mais na literatura brasileira, trabalhos que relacionam a pobreza ao desempenho macroeconomico mesmo sendo senso comum a relevância da atividade economica para a determinacao da condicao de pobreza. Assim sendo, o presente trabalho tentara inserir a discussao do combate a pobreza no âmbito da formulacao e da implementacao da politica macroeconomica considerando as relacoes entre o desempenho macroeconomico e a taxa de pobreza. Para investigar essas relacoes, utilizaremos um painel de dados formados por informacoes referentes aos estados brasileiros no periodo de 1981 a 2006. Como principal resultado, o trabalho evidencia que estrategias de crescimento economico, se tomadas isoladamente, sao insuficientes. Varios aspectos estruturais condicionam as relacoes entre o desempenho economico e o bem estar da populacao. Dessa forma, alem de que crescimento economico continuo, sustentavel e equilibrado, e necessario romper as estruturas que, principalmente nos paises subdesenvolvidos, levam ao conhecido processo de crescimento com concentracao de renda.