A Hierarchical Model of Reviews for Aspect based Sentiment Analysis
Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review's argumentative structure. Intuitively, sentences in a review build and elaborate upon each other; knowledge of the review structure and sentential context should thus inform the classification of each sentence. We demonstrate this hypothesis for the task of aspect based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM. We show that the hierarchical model outperforms two non hierarchical baselines, obtains results competitive with the state of the art, and outperforms the state of the art on five multilingual, multi domain datasets without any hand engineered features or external resources.
EMNLP