A Span-based Joint Model for Opinion Target Extraction and Target Sentiment
Classification
Abstract
Target-Based Sentiment Analysis aims at extracting
opinion targets and classifying the sentiment polarities expressed on each target. Recently, tokenbased sequence tagging methods have been successfully applied to jointly solve the two tasks,
which aims to predict a tag for each token. Since
they do not treat a target containing several words
as a whole, it might be difficult to make use of the
global information to identify that opinion target,
leading to incorrect extraction. Independently predicting the sentiment for each token may also lead
to sentiment inconsistency for different words in an
opinion target. In this paper, inspired by span-based
methods in NLP, we propose a simple and effective
joint model to conduct extraction and classification
at span level rather than token level. Our model first
emulates spans with one or more tokens and learns
their representation based on the tokens inside. And
then, a span-aware attention mechanism is designed
to compute the sentiment information towards each
span. Extensive experiments on three benchmark
datasets show that our model consistently outperforms the state-of-the-art methods