Analise de sentimento em documentos financeiros com múltiplas entidades

Abstract

Given the amount of information available on the internet, it becomes unfeasible the manual content analysis to identify information of interest. Among such analyses, one of particular interest is the polarity analysis, that is, the classi cation of a text document in positive, negative, and neutral, according to a certain topic. This task is particularly useful in the nance domain, where news about a company can a ect the performance of its stocks. Although most of the methods about this domain consider that documents have only one polarity, in fact most of the documents cite many entities and these entities are often the target of the polarity analysis. Thus, in this work, we intend to study strategies for polarity detection in nancial documents with multiple entities. In particular, we study methods based on the learning of multiple models, one for each observed entity, using SVM classi ers. We evaluate models based on the partition of documents according to the entities they cite and on the segmentation of documents into fragments according to the entities they cite. To segment documents we use several heuristics based on shallow and deep natural language proecssing. We found that entity-speci c models created by simply partitioning the document collection largely outperformed strategies based on single models.