Abstract
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.
Highlights
Sentiment analysis (Pang and Lee, 2008) is used to gauge public opinion towards products, to analyze customer satisfaction, and to detect trends
A visualization of the discourse structure according to Rhetorical Structure Theory (RST) (Mann and Thompson, 1988) for the example review in Figure 1 reveals that sentences
We show that the hierarchical model outperforms strong sentence-level baselines for aspect-based sentiment analysis, while achieving results competitive with the state-of-the-art and outperforming it on several datasets without relying on any hand-engineered features or sentiment lexica
Summary
Sentiment analysis (Pang and Lee, 2008) is used to gauge public opinion towards products, to analyze customer satisfaction, and to detect trends. With the proliferation of customer reviews, more fine-grained aspect-based sentiment analysis (ABSA) has gained in popularity, as it allows aspects of a product or service to be examined in more detail. Clauses are connected via different rhetorical relations, such as Elaboration and Background. Knowledge about the relations and the sentiment of surrounding sentences should inform the sentiment of the current sentence. If a reviewer of a restaurant has shown a positive sentiment towards the quality of the food, it is likely that his opinion will not change drastically over the course of the review. Overwhelmingly positive or negative sentences in the review help to disambiguate sentences whose sentiment is equivocal
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