Torii: An aspect-based sentiment analysis system that can mine conditions

Abstract

Aspect-based sentiment analysis systems are a kind of text-mining systems that specialize in summarizing the sentiment that a collection of reviews convey regarding some aspects of an item. There are many cases in which users write their reviews using conditional sentences; in such cases, mining the conditions so that they can be analyzed is very important not to misinterpret the corresponding sentiment summaries. Unfortunately, current commercial systems or research systems neglect conditions; current frameworks and toolkits do not provide any components to mine them; furthermore, the proposals in the literature are insufficient because they are based on handcrafted patterns that fall short regarding recall or machine learning procedures that are tightly bound with a specific language and require too much configuration. In this article, we present Torii, which is a system that loads a collection of reviews, discovers the aspects on which they report, and summarizes the sentiment that is conveyed on them taking into account the existing conditions, if any. We also describe its architecture, our approach to mine conditions, and our experimental analysis on a large multilingual data set with reviews from multiple categories. To the best of our knowledge, Torii is the first proposal that addresses aspect-based sentiment analysis taking conditions into account.