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Lechuzo : Weakly-Supervised System for Fine-Grained Sentiment Analysis

Traditional sentiment analysis approaches suffer from two major drawbacks: coarse granularity polar opinions can co-occur even within the same sentence, and ambiguity opinion-bearing terms can convey polar sentiment in different contexts. Consider the following laptop review: "the big plus was a large screen but having a large battery made me change my mind," where polar opinions co-occur in the same sentence, and the opinion term that describes the opinion targets (“large”) encodes polar sentiments: a positive for screen, and a negative for battery. To parse these differences, our approach is to identify opinions with respect to the specific opinion targets, while taking to take the context into account. Moreover, considering that there is a problem of obtaining an annotated training set in each context, our approach trains unsupervised model and cascading to a weakly-supervised model where the ground truth opinion for target aspects is never given.