Target-based sentiment analysis involves opinion target extraction and target sentiment classification. However, most of the existing works usually studied one of these two sub-tasks alone, which hinders their practical use. This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme. Our framework involves two stacked recurrent neural networks: The upper one predicts the unified tags to produce the final output results of the primary target-based sentiment analysis; The lower one performs an auxiliary target boundary prediction aiming at guiding the upper network to improve the performance of the primary task. To explore the inter-task dependency, we propose to explicitly model the constrained transitions from target boundaries to target sentiment polarities. We also propose to maintain the sentiment consistency within an opinion target via a gate mechanism which models the relation between the features for the current word and the previous word. We conduct extensive experiments on three benchmark datasets and our framework achieves consistently superior results.
As mentioned in Introduction, Target-based Sentiment Analysis are usually divided into two sub-tasks, namely, the Opinion Target Extraction task (OTE) and the Target Sentiment Classification (TSC) task. Although these two sub-tasks are treated as separate tasks and solved individually in most cases, for more practical applications, they should be solved in one framework. Given an input sentence, the output of a method should contain not only the extracted opinion targets, but also the sentiment predictions towards them. Some previous works attempted to discover the relationship between these two sub-tasks and gave a more integrated solution for solving the complete TBSA task. Concretely, @cite_17 employed Conditional Random Fields (CRF) together with hand-crafted linguistic features to detect the boundary of the target mention and predict the sentiment polarity. @cite_2 further improved the performance of the CRF based method by introducing a fully connected layer to consolidate the linguistic features and word embeddings. However, they found that a pipeline method can beat both of the model with joint training and the unified model. In this paper, we reexamine the task, and proposed a new unified solution which outperforms all previous reported methods.
@cite_2 @cite_17
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Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the sentiment towards each mention from a text corpus. The task is typically modeled as a sequence labeling problem, and solved using state-of-the-art labelers such as CRF. We empirically study the effect of word embeddings and automatic feature combinations on the task by extending a CRF baseline using neural networks, which have demonstrated large potentials for sentiment analysis. Results show that the neural model can give better results by significantly increasing the recall. In addition, we propose a novel integration of neural and discrete features, which combines their relative advantages, leading to significantly higher results compared to both baselines. We propose a novel approach to sentiment analysis for a low resource setting. The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity. This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them. We compare performance in both Spanish and English on microblog data, using only a sentiment lexicon as an external resource. By leveraging linguisticallyinformed features within conditional random fields (CRFs) trained to minimize empirical risk, our best models in Spanish significantly outperform a strong baseline, and reach around 90 accuracy on the combined task of named entity recognition and sentiment prediction. Our models in English, trained on a much smaller dataset, are not yet statistically significant against their baselines.