Abstract
It is well-known that opinions have targets. Extracting such targets is an important problem of opinion mining because without knowing the target of an opinion, the opinion is of limited use. So far many algorithms have been proposed to extract opinion targets. However, an opinion target can be an entity or an aspect (part or attribute) of an entity. An opinion about an entity is an opinion about the entity as a whole, while an opinion about an aspect is just an opinion about that specific attribute or aspect of an entity. Thus, opinion targets should be separated into entities and aspects before use because they represent very different things about opinions. This paper proposes a novel algorithm, called Lifelong-RL, to solve the problem based on lifelong machine learning and relaxation labeling. Extensive experiments show that the proposed algorithm Lifelong-RL outperforms baseline methods markedly.
Highlights
A core problem of opinion mining or sentiment analysis is to identify each opinion/sentiment target and to classify the opinion/sentiment polarity on the target (Liu, 2012)
We need to classify whether a target is an entity or an aspect because they refer to very different things
This paper studied the problem of classifying opinion targets into entities and aspects
Summary
A core problem of opinion mining or sentiment analysis is to identify each opinion/sentiment target and to classify the opinion/sentiment polarity on the target (Liu, 2012). The person is positive (opinion polarity) about the car (opinion target) as a whole, but slightly negative (opinion polarity) about the car’s engine (opinion target). We need to classify whether a target is an entity or an aspect because they refer to very different things. One can be positive about the whole entity (car) but negative about some aspects of it (e.g., engine) and vice versa. In supervised extraction one can annotate entities and aspects with separate labels in the training data to build a model to extract them separately, in this paper our goal is to help unsupervised target extraction methods to classify targets. Unsupervised target extraction methods are often preferred because they save the time-consuming data labeling or annotation step for each domain
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