Various Approaches to Aspect-based Sentiment Analysis
This is an incremental review of methods for a domain-specific task in natural language processing, with no novel contributions.
The paper tackles the problem of aspect-based sentiment analysis, which involves classifying sentiments for specific aspects in sentences, such as handling conflicting sentiments about different aspects of the same entity, but it does not present new results or concrete numbers.
The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as "the acceleration of this car is fast, but the reliability is horrible". This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.